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With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. In the present work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn-Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

The use of a sacrificial cathode additive that contains a large amount of lithium is one potential solution to compensate for the irreversible capacity loss associated with next-generation anodes such as silicon. Antifluorite-type Li6CoO4 has attracted attention as a potential cathode additive owing to its remarkably high theoretical lithium extraction capacity. However, the complex mechanism of lithium extraction as well as the oxygen loss from Li6CoO4 is not well understood. A generalizable computational thermodynamics and experimental framework is presented to understand the lithium-extraction pathway of Li6CoO4. It is found that one lithium per formula unit can be topotactically extracted from Li6CoO4, followed by an irreversible and nontopotactic phase transformation to Li2CoO3 or LiCoO2 depending on the temperature. The results show that peroxide species may form to charge-compensate for Li extraction which is undesirable as this can lead to gas release during battery operation. It is suggested that charging Li6CoO4 at an elevated temperature that the electrolyte can withstand, redirects the reaction pathway and prevents the formation of intermediate peroxide species making it an effective and stable sacrificial cathode additive.

Magnetic materials in reduced dimensions are not only excellent platforms for fundamental studies of magnetism, but they play crucial roles in technological advances. The discovery of intrinsic magnetism in monolayer two-dimensional van der Waals systems has sparked enormous interest, but the single-chain limit of one-dimensional magnetic van der Waals materials has been largely unexplored. Here, we report on a family of one-dimensional magnetic van der Waals materials with composition MX3 (M = Cr, V, and X = Cl, Br, I), prepared in fully-isolated fashion within the protective cores of carbon nanotubes. Atomic-resolution scanning transmission electron microscopy identifies unique structures that differ from the well-known 2D honeycomb lattice MX3 structure. Density functional theory (DFT) calculations reveal charge-driven reversible magnetic phase transitions. This article is protected by copyright. All rights reserved.

Dihedral angles in organic molecules and biomolecules are vital structural parameters that can be indirectly probed by nuclear magnetic resonance (NMR) measurements of vicinal J-couplings. The empirical relations that map the measured couplings to dihedral angles are typically determined by fitting using static structural models, but this neglects the effects of thermal fluctuations at the finite temperature conditions under which NMR measurements are often taken. In this study, we calculate ensemble-averaged J-couplings for several structurally rigid carbohydrate derivatives using first-principles molecular dynamics simulations to sample the thermally accessible conformations around the minimum energy structure. Our results show that including thermal fluctuation effects significantly shifts the predicted couplings relative to single-point calculations at the energy minima, leading to improved agreement with experiments. This provides evidence that accounting for conformational sampling in first-principles calculations can improve the accuracy of NMR-based structure determination for structurally complex carbohydrates.

Rational plastic recycling is critical for addressing the environmental challenges associated with plastic waste. Among the various recycling methods, chemical recycling, particularly via homogeneous catalysis, holds promise for converting plastic waste into valuable products. Post-consumer polymer wastes could present a challenge for catalytic upcycling due to the structural inhomogeneity and functionalization of the polyolefin chains. The impact of substrate aging on the performance of the upcycling catalyst can be viewed as an “inverse problem” of heterogeneous catalysis and has not received sufficient attention in mechanistic studies on this subject. Herein, we present a density functional theory study on the dehydrogenative upcycling of polyethylene (PE) with different in-chain impurities, representing the chemistry of post-consumption PE wastes. We selected the (tBu4POCOP)-Ir pincer complex catalyzed dehydrogenation of PE as our model reaction. The calculations reveal that common in-chain impurities found in PE, such as carbonyl, hydroxyl, epoxides, and chlorine atoms, inhibit the overall catalyst performance. These impurities form stable molecular complexes with the catalyst, leading to a substantial increase in the energy barriers of the initial reaction step, the C-H bond addition. We also observe that the reaction on the ideal crystalline PE is also impeded. However, highly distorted PE chains exhibit greater susceptibility toward the (tBu4POCOP)-Ir catalyst. Our mechanistic studies demonstrated that the reaction on the side alkane chains is kinetically favorable compared with the reaction on the PE backbone. The study highlights the critical role of in-chain heterogenieties in the catalytic activation of polymer chains and provides valuable insights into the development of effective technologies for upcycling plastic waste., ChemE/Inorganic Systems Engineering

A theoretical study is conducted with three MX₃ monolayer 2D materials (AlI₃, AsI₃, and IrBr₃) on their electronic properties and how a halogen monovacancy affects their thermostability. Density functional theory (DFT) calculations are run to obtain the band structures and phonon dispersions for both pristine and defective structures. It is shown that AlI₃ and AsI₃ have indirect bandgaps of 2.40 and 2.23 eV, respectively. IrBr₃ has a direct bandgap of 1.65 eV. Phonon dispersions indicate that they are all thermodynamically stable in pristine state, but their defective counterparts are not. Ab-initio molecular dynamics (AIMD) simulations are conducted for defective ones to further investigate their stability. It is found that AlI₃ and AsI₃ layers are decomposed while IrBr3 layer is bent. Further investigations are conducted by analyzing the bond energies and bond lengths of the three materials. It shows that AlI₃ and AsI₃ have lower bond energy and longer bond length, which makes them dissociated at the ambient temperature while higher bond energy and shorter bond length keep IrBr₃ stabilized and enable its displacive phase transition in displacive limit.

Ishii Akio. Elastic investigation for the existence of B33 phase in TiNi shape memory alloys using atomistically informed Eshelby’s ellipsoidal inclusion. Computational Materials Science, 218, 111954. https://doi.org/https://doi.org/10.1016/j.commatsci.2022.111954., The existence of the B33 phase in TiNi alloys, which was reported to be a stable phase using density functional theory calculations but not confirmed experimentally, is controversial. Using Eshelby’s ellipsoidal inclusion, which was atomistically informed by density functional theory calculations, we investigated the existence of the B33 phase in the TiNi shape memory alloy. The calculated total strains of the heterogeneously nucleated B33 phase were similar to the eigenstrains of the B19’ phase, which were also calculated using density functional theory calculations. Considering the similarity of the atomic structures of B33 and B19’, this indicates that the B33 phase was elastically suppressed and changed to the B19’ phase by the original B2 matrix. We confirmed that the elastic inhomogeneity between the B2 matrix and B33 phase plays a role in this change.

The reactivity of 5-[(E)-2-(4-hydroxyphenyl)ethen-1-yl]benzene-1,3-diol (trans-resveratrol) and related compounds toward electrogenerated superoxide radical anion (O2•–) were investigated using electrochemistry, in situ electrolytic electron spin resonance, and in situ electrolytic ultraviolet–visible spectral measurements, in N,N-dimethylformamide (DMF) with the aid of density functional theory (DFT) calculations. The quasi-reversible cyclic voltammogram of dioxygen/O2•– was modified by the presence of trans-resveratrol, suggesting that the electrogenerated O2•– was scavenged by trans-resveratrol through proton-coupled electron transfer (PCET) via three phenolic hydroxy groups (OH) on the stilbene moiety. The reactivity of trans-resveratrol toward O2•– characterized by the OHs was experimentally confirmed in comparative analyses using some related compounds, pinosylvin, pterostilbene, p-coumaric acid, and so on, in DMF. The electrochemical and DFT results suggested that a concerted PCET mechanism via 4′OH of trans-resveratrol proceeds, where the coplanarity of the two phenolic rings in the stilbene moiety linked by an ethylene bridge is essential for a successful O2•– scavenging.

Nature captures sunlight via light-absorbing molecules.Similarly, photosensitisers are used in applications of solar cells and artificial photosynthesis to absorb sunlight, and transfer the excited electron.Successful photosensitisers have in the past been based on a Ru polipyridyl scaffold, despite Ru being one of the scarcest elements in Earth's crust.This thesis work aims to replace Ru polipyridyl complexes by Fe carbene complexes, that by clever ligand design have approached suitable photosensitiser properties.One crucial property that is not yet competitive for Fe carbene photosensitisers is how long they stay in the excited state, i.e. their lifetime. This is controlled by the deactivation pathways of the molecule, dictated by the excited state landscape. Several Fe carbene photosensitisers were in this thesis investigated by spectroscopic and computational methods, to understand their deactivation pathways. For the Fe(II) carbene complexes investigated, small changes in the ligand structure influenced both what excited state (charge-transfer or metal-centred) that was mainly populated and the lifetime of the state.For the Fe(III) carbene complexes investigated, there was instead one dominating charge-transfer excited state that was rather unaffected by changes to the ligand. Furthermore, for the Fe(II) complexes metal-centred states played a large role in the deactivation pathway but for the Fe(III) complexes this was not the case.Also, one Co(III) carbene complex was investigated which displays remarkable long lifetime and emission from a metal-centred state.As a first step towards application, the electron-transfer properties of some of the photosensitisers were investigated.Fe(II) complexes with a push-pull design were able to transfer electrons to TiO2 in a solar cell configuration.The solar cell performance was however limited by an ultrafast recombination reaction, that brought a majority of the transferred electrons back to the photosensitiser.The Fe(I

Electrolyte effects play an important role in the activity of the oxygen reduction reaction (ORR) of Pt-based electrodes. Herein, we combine a computational model and rotating disk electrode measurements to investigate the effects from phosphate anion poisoning for the ORR on well-defined extended Pt surfaces. We construct a model including the poisoning effect from phosphate species on Pt(111) and Cu/Pt(111) based on density functional theory simulations. By varying the subsurface Cu content of the Cu/Pt(111) alloy, we tune the *OH binding energies on the surface by means of ligand effects, and as a result, we tune the ORR activity. We have investigated the effect of adsorbed phosphate species at low overpotentials when tuning *OH binding energies. Our results display a direct scaling relationship between adsorbed *OH and phosphate species. From the model, we observe how the three-fold binding sites of phosphate anions limit the packing of poisoning phosphate on the surface, thus allowing for *OH adsorption even when poisoned. Our work shows that, regardless of surface site blockage from phosphate, the trend in the catalytic oxygen reduction activity is predominantly governed by the *OH binding.

Drug delivery systems can improve the efficacy of therapeutic substances. In this investigation, the biocompatible Mg2(olsalazine) [Mg2(olz)] framework is investigated as a delivery system of phenylethylamine, dopamine and sertraline, substances related to the functioning of the Central Nervous System. Experiments previously reported show that Mg2(olz) encapsulates phenylethylamine and, under physiological conditions, releases the molecules. The hypothesis of this work is that this is possible due to the presence of non-covalent interactions. The non-covalent interactions between Mg2(olz) and phenylethylamine, dopamine and sertraline are analyzed using Quantum Theory of Atoms in Molecules and Non-covalent Interaction Index. It was found that the coordinatively unsaturated metal site of Mg2(olz) is important for the formation of non-covalent interactions. Due to similarity of systems of dopamine and sertraline with that of phenylethylamine, we conclude that Mg2(olz) is also capable to encapsulate and release these two drugs, being a good proposal as a drug delivery system.

Phenanthridine is the benzannulated version of the more well-known quinoline ligand fragment. Despite its promising electronic features, the inclusion of phenanthridine in ligands for transition metal coordination is currently underexplored. This work investigates the photophysical, spectroscopic, and electrochemical properties of transition metals complexes that contain the phenanthridine fragment in their ligand structure. Specifically, the complexes studied in this work are four-coordinate complexes of late transition metals – their structures range from tetrahedral to square planar, allowing us to examine the change in geometry as ligand structure changes. This document presents two projects. The first is an examination of the relationship between ligand Namido character and emission intensity in Zn(II) complexes. It includes the synthesis of three new Zn(II) complexes, the computational analysis of bonding, and the spectroscopic and computational investigation of the photophysical processes at work. The second project is a characterization of the Ni(II), Pd(II), and Cu(II) complexes of a new tetradentate NNNN diamido ligand. It presents the synthesis of the new ligand and its complexes, as well as the structural, spectroscopic, electrochemical, and computational analysis. Together, these projects demonstrate the versatility of the phenanthridine ligand fragment in coordination with late transition metals.

Elektron-Molekül-Reaktionen umfassen ein sehr breites Spektrum an Wechselwirkungen wie Anregung, Ionisierung, Bildung/Spaltung chemischer Bindungen usw. Daher sind solche Reaktionen für Wissenschaften von der Biologie und Materialwissenschaft bis hin zu Chemie und Physik von großer Wichtigkeit. Aus grundlegender Sicht besteht der erste Schritt zur Untersuchung dieser Wechselwirkungen darin, die Wellenfunktion der zu untersuchenden Moleküle zu ermitteln, indem die Schrödinger-Gleichung gelöst wird, da sie alle Informationen des Systems kodiert. Allerdings ist die Lösung dieser Gleichung für Systeme mit mehr als einem oder zwei Elektronen ohne Vereinfachungen analytisch nicht möglich. Selbst bei numerischen Lösungen steigt der Rechenaufwand für die Lösung exponentiell mit der Anzahl der Elektronen. Dies ist der Anwendungsbereich von ab-initio-Berechnungen, bei denen die Wellenfunktion zumindest in einem ersten Schritt durch Lösen einer Reihe kleinerer Einzelelektronenwellenfunktionen (oder Orbitale) für jedes der Elektronen in einem System erhalten wird. Ein Beispiel für eine Elektronen-Molekül-Reaktion ist die dissoziative Elektronenanlagerung (DEA), die in verschiedenen technologischen, atmosphärischen und biologischen Umgebungen eine wichtige Rolle spielt. Dabei zerfällt ein Molekül in Fragmente, nachdem es ein freies Elektron eingefangen hat. Eine ähnliche Art von Reaktion ist der dissoziative Elektronentransfer (DET), bei dem ein Elektron nicht eingefangen wird, sondern von einer atomaren oder molekularen Spezies auf eine andere übertragen wird, was zur Fragmentierung des Moleküls führt, das das Elektron empfängt. Im ersten Kapitel der Arbeit beschreibe ich die Theorie mehrerer ab-initio-Methoden, die für die Untersuchung wichtiger Elektronen-Molekül-Reaktionen wie DEA, die bei verschiedenen biologischen Wirkungen eine wichtige Rolle spielen, von grundlegender Bedeutung sind. Im zweiten Kapitel diskutiere ich, welche Bedeutung molekulare Kraftfelder in der comput, Electron-molecule reactions cover a very wide variety of interactions such as excitation, ionization, formation/cleavage of chemical bonds, etc. As such, this type of reactions is important in scientific fields ranging from biology and material science to chemistry and physics. From a fundamental point of view, the first step to study these interactions is to obtain the wave function of the participating molecule(s) by solving Schrödinger’s equation since it encodes all the information of the system. However, solving this equation is neither possible analytically for systems with more than one or two electrons without simplifications. Even in the case of numerical solutions the computational cost of solving it increases exponentially with the number of electrons. This is the main concern of ab initio calculations where the wave function is, at least in a first step, obtained by solving a set of smaller single electron wave functions (or orbitals) for each of the electrons in a system. An example of an electron-molecule reaction is Dissociative Electron Attachment (DEA) which plays a major role in different technological, atmospheric and biological environments. In it, a molecule dissociates into fragments after capturing a free electron. A similar type of reaction is Dissociative Electron Transfer (DET) where instead of capturing a free electron, an electron is transferred from an atomic or molecular species to another, leading to fragmentation of the molecule receiving the electron. In chapter one we describe the theory behind several ab initio methods that are fundamental for studying important electron-molecule reactions such as DEA which plays an important role in various biological processes. In Chapter two we discuss molecular force fields and their importance in computational quantum chemistry and we also describe three families of force fields commonly used in the literature. In chapter three a brief description of radiosensitizers and their application in b, Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers, Dissertation Universität Innsbruck 2023

The thermal conductivity and electrical resistivity of Zr, ZrC, and ZrN were calculated using first-principles density functional theory (DFT) and the Boltzmann transport equation. The electron-phonon scattering was modeled via the self-energy relaxation time approximation (SERTA), and the phonon-phonon scattering via the analogous single-mode relaxation time approximation (SMRTA). The results obtained from Abinit's electron-phonon coupling code EPH is in good agreement with experimental reference data for Zr and ZrN. Notably, the calculated electrical resistivity of ZrC was found to be significantly lower than the available reference data, likely due to deviations from a perfect Zr/C stoichiometric ratio in the experimental samples. Additionally, it was observed that the calculated lattice thermal conductivity was overestimated at low temperatures, possibly attributed to the neglect of electron-phonon scattering that otherwise appears in metallic systems., Värmeledningsförmågan och den elektriska resistiviteten för Zr, ZrC och ZrN beräknades med hjälp av täthetsfunktionalteori (DFT) och Boltzmanns transportekvation. Elektron-fononspridningen modellerades via 'self-energy relaxation time approximation' (SERTA), och fonon-fononspridningen via den motsvarande 'single-mode relaxation time approximation' (SMRTA). Resultaten från Abinits elektron-fononkopplingskod EPH stämmer väl överens med experimentella referensdata för Zr och ZrN. Den beräknade elektriska resistiviteten för ZrC visade sig emellertid vara betydligt lägre än all tillgänglig referensdata, troligtvis på grund av avvikelser från ett perfekt Zr/C-stökiometriskt förhållande i de experimentella proverna. Dessutom observerades att det beräknade fononbidraget till värmeledningsförmågan överskattades vid låga temperaturer, vilket möjligen beror på försummelsen av elektron-fononspridning som annars framträder i metalliska system.

The interfaces between metal electrodes and liquid electro-lytes are prototypical in electrochemistry. That is why it is crucial to have a molecular and dynamical understating of such interfaces for both electrical properties and chemical re-activities under potential control. In this short review, we will categorize different schemes for modeling electrified metal-electrolyte interfaces used in molecular dynamics simulations. Our focus is on the similarities between seemingly different methods and their conceptual connections in terms of relevant electrochemical quantities. Therefore, it can be used as a guideline for developing new methods and building modular-ized computational protocols for simulating electrified interfaces.

The interfaces between metal electrodes and liquid electro-lytes are prototypical in electrochemistry. That is why it is crucial to have a molecular and dynamical understating of such interfaces for both electrical properties and chemical re-activities under potential control. In this short review, we will categorize different schemes for modeling electrified metal-electrolyte interfaces used in molecular dynamics simulations. Our focus is on the similarities between seemingly different methods and their conceptual connections in terms of relevant electrochemical quantities. Therefore, it can be used as a guideline for developing new methods and building modular-ized computational protocols for simulating electrified interfaces.

We have set-up an electrochemical advanced oxidation process for ethidium bromide (1), based on the Eu-doped MnWO4 (Eu:MnWO4), obtained through a template-driven synthesis, along with developing a suitable monitoring method. Under galvanostatic conditions, Eu:MnWO4-coated graphite electrode serves as anode, applicable for removal of 1. To go further and augment the catalytic method, we have applied a modified carbon paste electrode for the monitoring of 1 with the limit of detection (LOD) of 54 nM. Enhancement of the hydrogen evolution reaction is an indication of electrocatalytic properties of the material, whereby developed method emerges as a candidate for straightforward application in electrochemical advanced oxidation processes (EAOPs). We have enriched experimental data with theoretical insights, provided by Density Functional Theory (DFT), and proposed oxidation mechanism of 1. Based on obtained results, we propose the new nanomaterial as a potent electrochemical modifier, suitable for catalytic treatment and process monitoring of the 1-polluted waters.

The aim of this study is to comprehensively examine the structural composition and properties of the AgAlS2 crystal during its high-pressure phase. This analysis delves into the second coordination environment of the crystal structure and elucidates the distinct transformations it undergoes during the phase transition. The band energy structure was calculated, and the origin of electronic levels was clarified. It is shown that the crystal becomes non-stratified during the phase transition. This study also determined the values of the crystal’s carrier effective masses, underscoring its spatial anisotropy. It was found that the calculated optical functions are similar to the crystal in the chalcopyrite structure, and their differences are shown. Further, this study involved the calculation of the crystal’s phonon spectrum, revealing the spectrum’s transformation during the phase transition. The vibrational frequencies were also obtained, with a symmetrical classification of vibrational modes. Finally, this study derived the infrared and Raman spectra of the AgAlS2 crystal, thereby providing a comprehensive picture of the crystal during its high-pressure phase.

Two new Zn(II) complexes bearing tridentate hydrazone-based ligands with NNO or NNS donor atoms were synthesised and characterised by elemental analysis, infrared (IR) and nuclear magnetic resonance (NMR) spectroscopies, and single crystal X-ray diffraction methods. These complexes, together with four previously synthesised analogues, having hydrazone ligands with a NNO donor set of atoms, were successfully employed as catalysts in the ketone-amine-alkyne (KA2) coupling reaction, furnishing tetrasubstituted propargylamines, compounds with unique applications in organic chemistry. DFT calculations at the CAM-B3LYP/TZP level of theory were performed to elucidate the electronic structure of the investigated Zn(II) complexes, excellently correlating the structure of the complexes to their catalytic reactivity.

The effects of alkyl substituents on stacking interactions of aromatic ligands in organometallic compounds were studied on two most common ligands with substituents pentamethylcyclopentadienyl (Cp*) and 1-methyl-4-isopropylbenzene (p-cymene). The analysis ofgeometries of interactions found in crystal structures deposited in the Cambridge Structural Database indicated that substituents are involved in interactions with aromatic rings, indicating the combination of stacking and C-and p-cymene ligands. Quantum chemical calculations on p-cymene half-sandwich ruthenium(II) compound showed that additional CB97D2/def2-TZVP interaction energy between two p-cymenecomplexes is -7.56 kcal/mol, while the interaction energy between two corresponding benzene complexes is -3.93 kcal/mol. Ruthenium p-cymene complexes can engage in stacking interactions with large horizontal displacements combined with interactions between the substituents, with a significant interaction energy of -4.57 kcal/mol. These studies show that substituents strengthen stacking interactions of coordinated aromatic rings and modulate their geometries by providing additional simultaneous interactions.

Single-atom catalysts (SACs) are heterogenous catalysts with elements in common with coordination compounds. We discuss some fundamental elements required for the successful computational modeling of SACs for electrocatalytic applications. The first two aspects are the role played by the exchange-correlation functional adopted within a given DFT approach and the role of the local coordination of the active transition metal atom. Next, we discuss new intermediates that can form on SACs and that are not present on extended metal electrodes and how to model solvation, with particular emphasis on the fact that on SACs water can not only act as a solvent but also as a ligand. Finally, we discuss challenges related to the inclusion of pH and voltage in the models and some open issue concerning the rational design of new SACs.

Le déploiement des batteries métal-air rechargeables, des piles à combustible réversibles et des électrolyseurs est retardé par la difficulté de synthétiser en grande quantité des catalyseurs à bas coût. Ici, des nanoparticules de MnxCo1-xFe2O4 (0 ≤x≥1) avec une cristallinité élevée et une composition homogène, synthétisées par induction plasma thermique sont étudiées. Les nanoparticules ont une taille médiane de 40 nm et une forme d'octaèdre tronqué bien définie avec des facettes {100} et {111} principalement exposées. Des études électrochimiques dans du KOH 0,1 M révèlent une activité la plus élevée pour le noir de carbone+Mn0,5Co0,5Fe2O4: un potentiel demi-onde de réduction de l'oxygène (ORR) 140 mV plus négatif que celui de 20 % en poids de Pt/C ; un surtention de dégagement d'oxygène (OER) de 420 mV à 10 mA cm-2 (identique à IrO2/C). Ce sont parmi les meilleures performances rapportées pour les ferrites, compte tenu de la taille des nanocristaux. Des études computationnelles indiquent que l'adsorption et l'activation de la molécule d'oxygène sont favorisées sur Mn0.5Co0.5Fe2O4 et suggèrent une activité électrocatalytique plus élevée pour les facettes {111} par rapport au {100}. La thèse étudie également l'utilisation potentielle de La(OH)3 comme substrat pour les nanoparticules de Ni, où des catalyseurs hybrides comprenant des nanoparticules de Ni ancrées sur des nanotiges de La(OH)3 et supportées sur des flocons d'oxyde de graphène réduit sont préparés. Le meilleur Ni+La(OH)3/rGO montre un potentiel demi-onde ORR (E1/2) de 0,715 V vs RHE, réduit l'oxygène par un processus à quatre électrons et nécessite un potentiel de 1,574 V vs RHE pour atteindre une densité de courant OER de 1 mA cm-2 dans 0,1 M KOH. Ce dernier est significativement meilleur que l'IrO2/C commercial (E10=1,663 V vs RHE) pour l'OER. L'activité catalytique globale du système Ni+La(OH)3 est considérablement améliorée en raison de la charge d'acquisition de Ni du substrat La(OH)3, comme indiqué

We provide a comprehensive theory of magnetic phases in two-dimensional triangulene crystals, using both Hubbard model and density functional theory (DFT) calculations. We consider centrosymmetric and noncentrosymmetric triangulene crystals. In all cases DFT and the mean-field Hubbard model predict the emergence of broken-symmetry antiferromagnetic (ferrimagnetic) phases for the centrosymmetric (noncentrosymmetric) crystals. This includes the special case of the [4,4]triangulene crystal, whose noninteracting energy bands feature a gap with flat valence and conduction bands. We show how the lack of contrast between the local density of states of these bands, recently measured via scanning tunneling spectroscopy, is a natural consequence of a broken-symmetry Néel state that blocks intermolecular hybridization. Using random phase approximation, we also compute the spin wave spectrum of these crystals, including the recently synthesized [4,4]triangulene crystal. The results are in excellent agreement with the predictions of a Heisenberg spin model derived from multiconfiguration calculations for the unit cell. We conclude that experimental results are compatible with an antiferromagnetically ordered phase where each triangulene retains the spin predicted for the isolated species.

The interfaces between metal electrodes and liquid electro-lytes are prototypical in electrochemistry. That is why it is crucial to have a molecular and dynamical understating of such interfaces for both electrical properties and chemical re-activities under potential control. In this short review, we will categorize different schemes for modeling electrified metal-electrolyte interfaces used in molecular dynamics simulations. Our focus is on the similarities between seemingly different methods and their conceptual connections in terms of relevant electrochemical quantities. Therefore, it can be used as a guideline for developing new methods and building modular-ized computational protocols for simulating electrified interfaces.

The coexistence of the charge-density wave (CDW) and the superconducting phase and their tunability under external pressure remains one of the key focus points in understanding the electronic structure of AV3Sb5 (A = K, Rb, Cs) kagome metals. Here, we employ synchrotron-based infrared spectroscopy assisted by density-functional calculations to study the pressure evolution of the electronic structure at room temperature up to 17 GPa experimentally for the first time. The optical spectrum of CsV₃Sb₅ is characterized by the presence of localized carriers seen as a broad peak at finite frequencies in addition to the conventional metallic Drude response. The pressure dependence of this low-energy peak mirrors the re-entrant behavior of superconductivity and may be interpreted in terms of electron-phonon coupling changing with the growth and shrinkage of the Fermi surface (FS). Moreover, drastic modifications in the low-energy interband absorptions are observed upon the suppression of CDW. These changes are related to the upward shift of the Sb2 px +py band that eliminates part of the FS around the M-point, whereas band saddle points do not change significantly. These observations shed new light on the mixed electronic and lattice origin of the CDW in CsV₃Sb₅.

This paper explores the optical properties of an exfoliated MoSe2 monolayer implanted with Cr + ions, accelerated to 25 eV. Photoluminescence of the implanted MoSe 2 reveals an emission line from Cr-related defects that is present only under weak electron doping. Unlike band-to-band transition, the Cr-introduced emission is characterized by nonzero activation energy, long lifetimes, and weak response to the magnetic field. To rationalize the experimental results and get insights into the atomic structure of the defects, we modeled the Cr-ion irradiation process using ab initio molecular dynamics simulations followed by the electronic structure calculations of the system with defects. The experimental and theoretical results suggest that the recombination of electrons on the acceptors, which could be introduced by the Cr implantation-induced defects, with the valence band holes is the most likely origin of the low-energy emission. Our results demonstrate the potential of low-energy ion implantation as a tool to tailor the properties of two-dimensional (2D) materials by doping.

The interlayer interaction in Pt-dichalcogenides strongly affects their electronic structures. The modulations of the interlayer atom-coordination in vertical heterostructures based on these materials are expected to laterally modify these interlayer interactions and thus provide an opportunity to texture the electronic structure. To determine the effects of local variation of the interlayer atom coordination on the electronic structure of PtSe₂, van der Waals heterostructures of PtSe₂ and PtTe₂ have been synthesized by molecular beam epitaxy. The heterostructure forms a coincidence lattice with 13-unit cells of PtSe₂ matching 12-unit cells of PtTe₂, forming a moiré superstructure. The interaction with PtTe₂ reduces the band gap of PtSe₂ monolayers from 1.8 to 0.5 eV. While the band gap is uniform across the moiré unit cell, STS and dI/dV mapping identify gap states that are localized within certain regions of the moiré unit cell. Deep states associated with chalcogen pz-orbitals at binding energies of ~-2 eV also exhibit lateral variation within the moiré unit cell, indicative of varying interlayer chalcogen interactions. Density functional theory calculations indicate that local variations in atom coordination in the moiré unit cell causes variations in the charge transfer from PtTe2 to PtSe2 thus affecting the value of the interface dipole. Experimentally this is confirmed by measuring the local work function by field emission resonance spectroscopy, which reveals a large work function modulation of ~0.5 eV within the moiré structure. These results show that the local coordination variation of the chalcogen atoms in the PtSe2/PtTe2 van der Waals heterostructure induces a nanoscale electronic structure texture in PtSe₂.

Electron dynamics of anatase TiO2 under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO2 to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of 1×1010 to 9×1012 W/cm2, with a transition from multiphoton to tunneling ionization occurring at 9×1012 W/cm2. We observe a sudden increase in energy and the number of excited electrons beyond 1×1013 W/cm2, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO2 for 800 nm to be 0.1 J/cm2. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index (n2) of TiO2 to be 3.54×10−11 cm2/W.

In deep geological repositories for nuclear waste, the surrounding rock formation serves as an important barrier against radionuclide migration. Multiple potential host rocks contain phyllosilicates, which have shown high efficiency in radionuclide sorption. Recent experimental studies reported a heterogeneous distribution of adsorbed radionuclides on nanotopographic mineral surfaces. In this study, we investigated the energetic differences of surface sorption sites available at nanotopographic structures such as steps, pits, and terraces. Eleven important surface sites were selected and the energies of ad- and desorption reactions were obtained from density functional theory calculations. The adsorption energies were then used for the parameterization of a kinetic Monte Carlo model simulating the distribution of adsorbed europium on a typical nanotopographic muscovite surface. On muscovite, silicon step sites are favorable for europium sorption and lead to an increased adsorption in regions with high step concentrations. Under identical chemical conditions, sorption on typical nanotopographic surfaces is increased by a factor of three compared to atomically flat surfaces. Desorption occurs preferentially at terrace sites, leading to an overall 2.5 times increased retention at nanotopographic structures. This study provides a mechanistic explanation for heterogeneous sorption on nanotopographic mineral surfaces due to the availability of energetically favorable sorption sites.

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future.

Quantum mechanical calculations of the electronic structure of matter enable accessing interesting thermodynamical properties without the need for prior experimental measurements. Therefore, electronic structure calculations are of great interest in fields such as materials discovery or drug design. At the forefront of such simulations lies density functional theory (DFT), due to its excellent balance between computational accuracy and efficiency. Yet, as pressing environmental and social issues shift the research focus to increasingly complicated systems and conditions, even the most efficient of DFT implementations are approaching their limitations in terms of computational feasibility. A possible route to enable more complex calculations lies with machine learning (ML), i.e., algorithms that are capable of capturing complicated relationships based on large amounts of data.

Density Functional Theory (DFT) is a widely used method for computing numerous properties in materials science and chemistry. Despite its usefulness, its inherent computational scaling with system size often limits its applicability and makes large-scale simulations infeasible. Our team develops a machine-learning approach to accelerate Kohn-Sham DFT calculations using deep neural networks within the Materials Learning Algorithms (MALA) Python framework. Our method employs bispectrum descriptors to encode roto-translationally equivariant representations of atomic neighborhoods as input to the neural networks. The networks then predict the local density of states (LDOS) from which various quantities of interest can be calculated. Our MALA models are trained on DFT data which serve as the ground-truth for the machine-learning models. Our MALA models significantly reduce the computational demands compared to conventional DFT methods. In this poster, we demonstrate the efficacy of our approach on a system of hydrogen molecules under various pressure and temperature conditions, showcasing the potential of our methods for molecular systems. Furthermore, we explore the use of SE(3)-equivariant graph neural networks (equiformer GNNs) to enhance the generalizability and extrapolation capabilities of our models while further reducing the computational cost. Our results indicate that the MALA framework provides a powerful and efficient tool for accelerating Kohn-Sham DFT calculations in molecular systems. The proposed approach has the potential to revolutionize the field of materials science by enabling researchers to perform large-scale simulations and explore complex molecular systems more efficiently. This work paves the way for future research in developing advanced machine-learning algorithms for accelerating electronic structure calculations and determining properties in materials science and chemistry both accurately and efficiently.

Due to its balance between accuracy and computational cost, Density Functional Theory (DFT) is one of the most important computational methods within materials science and chemistry. However, current research efforts such as the modeling of matter under extreme conditions demand the application of DFT to larger length scales as well as higher temperatures. Such investigations are currently prohibited due to the computational scaling of DFT. We have recently introduced a machine-learning workflow that replaces modern DFT calculations [1,2,3]. This workflow uses neural networks to predict the electronic structure locally. We show that by employing such an approach, models can be trained to predict the electronic structure of matter across temperature ranges. This paves the way for large-scale simulations of thermodynamically sampled observables relevant to modeling technologically important phenomena such as radiation damage in fusion reactor walls.

Density Functional Theory (DFT) is a widely used method for computing numerous properties in materials science and chemistry. Despite its usefulness, its inherent computational scaling with system size often limits its applicability and makes large-scale simulations infeasible. Our team develops a machine-learning approach to accelerate Kohn-Sham DFT calculations using deep neural networks within the Materials Learning Algorithms (MALA) Python framework. Our method employs bispectrum descriptors to encode roto-translationally equivariant representations of atomic neighborhoods as input to the neural networks. The networks then predict the local density of states (LDOS) from which various quantities of interest can be calculated. Our MALA models are trained on DFT data which serve as the ground-truth for the machine-learning models. Our MALA models significantly reduce the computational demands compared to conventional DFT methods. In this poster, we demonstrate the efficacy of our approach on a system of hydrogen molecules under various pressure and temperature conditions, showcasing the potential of our methods for molecular systems. Furthermore, we explore the use of SE(3)-equivariant graph neural networks (equiformer GNNs) to enhance the generalizability and extrapolation capabilities of our models while further reducing the computational cost. Our results indicate that the MALA framework provides a powerful and efficient tool for accelerating Kohn-Sham DFT calculations in molecular systems. The proposed approach has the potential to revolutionize the field of materials science by enabling researchers to perform large-scale simulations and explore complex molecular systems more efficiently. This work paves the way for future research in developing advanced machine-learning algorithms for accelerating electronic structure calculations and determining properties in materials science and chemistry both accurately and efficiently.

In this presentation, I will present our recent progress in integrating machine learning to significantly boost the computational efficiency of electronic structure calculations [1]. I will specifically address our efforts to speed up density functional theory calculations, for which we have developed the Materials Learning Algorithms framework [2]. Our findings illustrate significant improvements in calculation speed for metals at their melting point. Additionally, our use of automated machine learning has yielded significant reductions in computational resources required to identify optimal neural network architectures, laying the groundwork for extensive investigations [3]. Furthermore, I will show the transferability of our ML model across temperatures [4]. Most importantly, I will present our latest breakthrough, which enables fast neural-network driven electronic structure calculations for systems unattainable by conventional density functional theory calculations [5]. References [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials, 6, 040301 (2022). [2] J. Ellis, L. Fiedler, G. Popoola, N. Modine, J. Stephens, A. Thompson, A. Cangi, S. Rajamanickam, Phys. Rev. B, 104, 035120 (2021). [3] L. Fiedler, N. Hoffmann, P. Mohammed, G. Popoola, T. Yovell, V. Oles, J. Austin Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol., 3, 045008 (2022). [4] L. Fiedler, N. A. Modine, K. D. Miller, and A. Cangi Phys. Rev. B 108, 125146 (2023). [5] L. Fiedler, N. Modine, S. Schmerler, D. Vogel, G. Popoola, A. Thompson, S. Rajamanickam, A. Cangi, npj. Comput. Mater., 9, 115 (2023).

Artificial intelligence (AI) has great potential for accelerating electronic structure calculations to hitherto unattainable scales [1]. I will present our recent efforts accomplishing speeding up Kohn-Sham density functional theory calculations at finite temperatures with deep neural networks in terms of our Materials Learning Algorithms framework [2,3] by illustrating results for metals across their melting point. Furthermore, our results towards automated machine learning save orders of magnitude in computational efforts for finding suitable neural networks and set the stage for large-scale AI-driven investigations [4]. Finally, I will conclude with a preview of our most recent result that enables neural-network-driven electronic structure calculations for systems containing more than 100,000 atoms. [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022).

In this talk, I will present two lines of our research. In the first part, I will discuss the application of time-dependent density functional theory (TDDFT) in modeling observables induced in matter under extreme conditions, such as the electron loss function relevant to scattering experiments with X-ray free electron lasers [1] and the electrical conductivity in metals [2]. In the second part, I will highlight our recent progress in leveraging Artificial Intelligence (AI) to enhance the efficiency of electronic structure calculations [3]. Specifically, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations at finite temperatures by integrating deep neural networks into the Materials Learning Algorithms framework [4,5]. Our results demonstrate significant improvements in calculation speed for metals up to their melting point. Additionally, our implementation of automated machine learning has led to substantial savings in computational resources for identifying optimal neural network architectures, paving the way for large-scale AI-driven investigations [6]. Lastly, I will present our latest breakthrough, showcasing the capability of neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [7]. [1] Z. Moldabekov, T. Dornheim, A. Cangi, Sci. Rep. 12, 1093 (2022). [2] K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, Phys. Rev. B 107, 115131 (2023). [3] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [4] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [5] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [6] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Ca

Machine learning has emerged as a powerful technique for processing large and complex datasets. Recently it has been utilized for both improving the accuracy and accelerating the computational speed of electronic structure theory. In this chapter, we provide the theoretical background of both density functional theory, the most widely used electronic structure method, and machine learning on a generally accessible level. We provide a brief overview of the most impactful results in recent times. We, further, showcase how machine learning is used to advance static and dynamic electronic structure calculations with concrete examples. This chapter highlights that fusing concepts of machine learning and density functional theory holds the promise to greatly advance electronic structure calculations enabling unprecedented applications for in-silico materials discovery and the search for novel chemical reaction pathways.

Interactions between electrons and nuclei, the principal building blocks of matter, determine all materials properties. Understanding and modeling these interactions therefore is of paramount importance to pressing scientific questions, e.g., in the context of renewable energy solutions or sustainable materials. However, electronic structure simulations often face a trade-off between accuracy and system size . One may simulate materials at quantum-accuracy, but can only do so for a few thousand atoms, even with the most advanced electronic structure tools, such as density functional theory (DFT). Conversely, large-scale simulations suffer from drastically reduced predictive power due to necessary approximations. The Materials Learning Algorithms (MALA) package tackles this challenge by combining neural networks, physically constrained optimization algorithms, and efficient post-processing routines to construct machine-learning models of DFT (ML-DFT). Unlike existing ML approaches, MALA creates ML-DFT models that completely replace DFT, providing access to both scalar quantities like energies and volumetric information about the electronic structure, such as the electronic density. We have demonstrated that MALA can be used with any number of atoms (successfully tested with 100’000 atoms), covering a wide range of temperatures and pressures. MALA enables a promising approach for materials modeling at unattained scale and accuracy.

In this talk, I will present our recent advancements in utilizing machine learning to significantly enhance the efficiency of electronic structure calculations [1]. In particular, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations at finite temperatures by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate substantial gains in calculation speed for metals across their melting point. Furthermore, our implementation of automated machine learning has resulted in significant savings in computational resources when identifying optimal neural network architectures, thereby laying the foundation for large-scale investigations [4]. I will also showcase our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3, 045008 (2022). [5] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022).

Quantum mechanical calculations of the electronic structure of matter enable accessing interesting thermodynamical properties without the need for prior experimental measurements. Therefore, electronic structure calculations are of great interest in fields such as materials discovery or drug design. At the forefront of such simulations lies density functional theory (DFT), due to its excellent balance between computational accuracy and efficiency. Yet, as pressing environmental and social issues shift the research focus to increasingly complicated systems and conditions, even the most efficient of DFT implementations are approaching their limitations in terms of computational feasibility. A possible route to enable more complex calculations lies with machine learning (ML), i.e., algorithms that are capable of capturing complicated relationships based on large amounts of data.

Due to its balance between accuracy and computational cost, Density Functional Theory (DFT) is one of the most important computational methods within materials science and chemistry. However, current research efforts such as the modeling of matter under extreme conditions demand the application of DFT to larger length scales as well as higher temperatures. Such investigations are currently prohibited due to the computational scaling of DFT. We have recently introduced a machine-learning workflow that replaces modern DFT calculations [1,2,3]. This workflow uses neural networks to predict the electronic structure locally. We show that by employing such an approach, models can be trained to predict the electronic structure of matter across temperature ranges. This paves the way for large-scale simulations of thermodynamically sampled observables relevant to modeling technologically important phenomena such as radiation damage in fusion reactor walls.

In this talk, I will present our recent advancements in utilizing Artificial Intelligence (AI) to significantly enhance the efficiency of electronic structure calculations [1]. In particular, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations atfinite temperatures by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate substantial gains in calculation speed for metals across their melting point. Furthermore, our implementation of automated machine learning hasresulted in significant savings in computational resources when identifying optimal neural network architectures, thereby laying the foundation forlarge-scale AI-driven investigations [4]. I will also showcase our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. Finally, I will provide an outlook on the potential of physics-informed neural networks for solving time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [6]. [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022). [5] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022). [6] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Eq

In this talk, I will present our recent advancements in utilizing Artificial Intelligence (AI) to significantly enhance the efficiency of electronic structure calculations [1]. In particular, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations atfinite temperatures by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate substantial gains in calculation speed for metals across their melting point. Furthermore, our implementation of automated machine learning hasresulted in significant savings in computational resources when identifying optimal neural network architectures, thereby laying the foundation forlarge-scale AI-driven investigations [4]. I will also showcase our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. Finally, I will provide an outlook on the potential of physics-informed neural networks for solving time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [6]. [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022). [5] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022). [6] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Eq

In this talk, I will present our recent advancements in utilizing Artificial Intelligence (AI) to significantly enhance the efficiency of electronic structure calculations [1]. In particular, I will focus on our efforts to accelerate Kohn-Sham density functional theory calculations atfinite temperatures by incorporating deep neural networks within the Materials Learning Algorithms framework [2,3]. Our results demonstrate substantial gains in calculation speed for metals across their melting point. Furthermore, our implementation of automated machine learning hasresulted in significant savings in computational resources when identifying optimal neural network architectures, thereby laying the foundation forlarge-scale AI-driven investigations [4]. I will also showcase our most recent breakthrough, which enables neural-network-driven electronic structure calculations for systems containing over 100,000 atoms [5]. Finally, I will provide an outlook on the potential of physics-informed neural networks for solving time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [6]. [1] L. Fiedler, K. Shah, M. Bussmann, A. Cangi, Phys. Rev. Materials 6, 040301, (2022). [2] A. Cangi, J. A. Ellis, L. Fiedler, D. Kotik, N. A. Modine, V. Oles, G. A. Popoola, S. Rajamanickam, S. Schmerler, J. A. Stephens, A. P. Thompson, MALA, https://doi.org/10.5281/zenodo.5557254 (2021). [3] J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, Phys. Rev. B 104, 035120 (2021). [4] L. Fiedler, N. Hoffmann, P. Mohammed, G. A. Popoola, T. Yovell, V. Oles, J. A. Ellis, S. Rajamanickam, A. Cangi, Mach. Learn.: Sci. Technol. 3 045008 (2022). [5] L. Fiedler, N. A. Modine, S. Schmerler, D. J. Vogel, G. A. Popoola, A. P. Thompson, S. Rajamanickam, A. Cangi, arXiv:2210.11343 (2022). [6] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, Physics-Informed Neural Networks as Solvers for the Time-Dependent Schrödinger Eq

Due to its balance between accuracy and computational cost, Density Functional Theory (DFT) is one of the most important computational methods within materials science and chemistry. However, current research efforts such as the modeling of matter under extreme conditions demand the application of DFT to larger length scales as well as higher temperatures. Such investigations are currently prohibited due to the computational scaling of DFT. We have recently introduced a machine-learning workflow that replaces modern DFT calculations [1,2,3]. This workflow uses neural networks to predict the electronic structure locally. We show that by employing such an approach, models can be trained to predict the electronic structure of matter across temperature ranges. This paves the way for large-scale simulations of thermodynamically sampled observables relevant to modeling technologically important phenomena such as radiation damage in fusion reactor walls.

In this talk, I will present our research on the application of time-dependent density functional theory (TDDFT) to model observables induced in matter under extreme conditions. Specifically, I will discuss the electron loss function in scattering experiments with X-ray free electron lasers [1] and the electrical conductivity in metals [2, 3]. Additionally, I will explore the potential of physics-informed neural networks for solving the time-dependent Kohn-Sham equations, which describe electron dynamics in response to incident electromagnetic waves [4]. [1] Z. Moldabekov, T. Dornheim, A. Cangi, Sci. Rep. 12, 1093 (2022). [2] K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, Phys. Rev. B 107, 115131 (2023). [3] K. Ramakrishna, M. Lokamani, A. Baczewski, J. Vorberger, A. Cangi, arXiv:2210.10132 (2022). [4] K. Shah, P. Stiller, N. Hoffmann, A. Cangi, NeurIPS Machine Learning and the Physical Sciences, arXiv:2210.12522 (2022).

Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein a new machine-learned ab-initio derived molecular-spin dynamics (MSD) methodology with explicit treatment for longitudinal spin-fluctuations is utilized to probe the dynamic phase-diagram of iron. This framework uniquely enables an accurate resolution of the phase-transition kinetics and Earth-core elastic properties, as highlighted by compressional wave velocity and adiabatic bulk moduli measurements. In addition, a unique coupling of MSD with time-dependent density functional theory enables gauging electronic transport properties, critically important for resolving geodynamo dynamics.

Electron dynamics of anatase TiO2 under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO2 to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of 1×1010 to 9×1012 W/cm2, with a transition from multiphoton to tunneling ionization occurring at 9×1012 W/cm2. We observe a sudden increase in energy and the number of excited electrons beyond 1×1013 W/cm2, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO2 for 800 nm to be 0.1 J/cm2. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index (n2) of TiO2 to be 3.54×10−11 cm2/W.