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Machine learning potentials (MLP) have revolutionized the field of atomistic simulations by describing the atomic interactions with the accuracy of electronic structure methods at a small fraction of the costs. Most current MLPs construct the energy of a system as a sum of atomic energies, which depend on information about the atomic environments provided in form of predefined or learnable feature vectors. If, in addition, non-local phenomena like long-range charge transfer are important, fourth-generation MLPs need to be used, which include a charge equilibration (Qeq) step to take the global structure of the system into account. This Qeq can significantly increase the computational cost and thus can become the computational bottleneck for large systems. In this paper we present a highly efficient formulation of Qeq that does not require the explicit computation of the Coulomb matrix elements resulting in a quasi-linearly scaling method. Moreover, our approach also allows for the efficient calculation of energy derivatives, which explicitly consider the global structure-dependence of the atomic charges as obtained from Qeq. Due to its generality, the method is not restricted to MLPs but can also be applied within a variety of other force fields.

In the dynamic field of materials science, the quest to find optimal structures with low potential energy is of great significance. Over the past two decades, the minima hopping algorithm has emerged as a successful tool in this pursuit. We present a robust, user friendly and efficient implementation of the minima hopping algorithm as a Python library, enhancing in this way the global structure optimization simulations significantly. Our implementation significantly accelerates the exploration the potential energy surfaces, leveraging an MPI parallelization scheme that allows for multi level parallelization. In this scheme, multiple minima hopping processes are running simultaneously communicating their findings to a single database and, therefore, sharing information with each other about which parts of the potential energy surface have already been explored. Also multiple features from several existing implementations such as variable cell shape molecular dynamics and combined atomic position and cell geometry optimization for bulk systems, enhanced temperature feedback and fragmentation fixing for clusters are included in this implementation. Finally, this implementation takes advantage of the Atomic Simulation Environment (ASE) Python library allowing for high flexibility regarding the underlying energy and force evaluation.

This paper presents the results of an extensive structural search of ternary solids containing lutetium, nitrogen and hydrogen. Based on thousands of thermodynamically stable structures, available online, the convex hull of the formation enthalpies is constructed. To obtain the correct energetic ordering, the highly accurate RSCAN DFT functional is used in high quality all-electron calculations. In this way possible pseudopotential errors are eliminated. A novel lutetium hydride structure (HLu$_2$) that is on the convex hull is found in our search. An electron phonon analysis however shows that it is not a candidate structure for near ambient superconductivity. Besides this structure, which appears to have been missed in previous searches, possibly due to different DFT methodologies, our results agree closely with the results of previously published structure search efforts. This shows, that the field of crystal structure prediction has matured to a state where independent methodologies produce consistent and reproducible results, underlining the trustworthiness of modern crystal structure predictions. Hence it is quite unlikely that a structure, that would give rise within standard BCS theory to the superconducting properties, claimed to have been observed by Dasenbrock-Gammon et al. 10.1038/s41586-023-05742-0 , exists. This solidifies the evidence that no structure with conventional superconducting properties exists that could explain the experimental observation made by Dasenbrock-Gammon et al. 10.1038/s41586-023-05742-0

In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions, the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors. In this work we show that including -- in addition to structural information -- the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments. The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt., Comment: 41 pages, 7 figures, accepted

Ground state structures found in nature are in many cases of high symmetry. But structure prediction methods typically render only a small fraction of high symmetry structures. Especially for large crystalline unit cells there are many low energy defect structures. For this reason methods have been developed where either preferentially high symmetry structures are used as input or where the whole structural search is done within a certain symmetry group. In both cases it is necessary to specify the correct symmetry group beforehand. However it can in general not be predicted which symmetry group is the correct one leading to the ground state. For this reason we introduce a potential energy biasing scheme that favors symmetry and where it is not necessary to specify any symmetry group beforehand. On this biased potential energy surface, high symmetry structures will be found much faster than on an unbiased surface and independently of the symmetry group to which they belong. For our two test cases, a $C_{60}$ fullerene and bulk silicon carbide, we get a speedups of 25 and 63. In our data we also find a clear correlation between the similarity of the atomic environments and the energy. In low energy structures all the atoms of a species tend to have similar environments., Comment: 8 pages, 4 figures, 1 table

A fast and reliable geometry optimization algorithm is presented that optimizes atomic positions and lattice vectors simultaneously. Using a series of benchmarks, it is shown that the method presented in this paper outperforms in most cases the standard optimization methods implemented in popular codes such as QUANTUM ESPRESSO and VASP. To motivate the variable cell shape optimization method presented in here, the eigenvalues of the lattice Hessian matrix are investigated thoroughly. It is shown that they change depending on the shape of the cell and the number of particles inside the cell. For certain cell shapes the resulting condition number of the lattice matrix can grow quadratically with respect to the number of particles. By a coordinate transformation which can be applied to all variable cell shape optimization methods, the undesirable conditioning of the lattice Hessian matrix is eliminated.

To elucidate the geometric structure of the putative room temperature superconductor, carbonaceous sulfur hydride, at high pressure, we present the results of an extensive computational structure search of bulk C-S-H at 250 gigapascals. Using the minima hopping structure prediction method coupled to the GPU accelerated Sirius library, more than 17,000 local minima with different stochiometries in large simulation cells were investigated. Only 24 stochiometries are favourable against elemental decomposition, all of them are carbon doped H$_3$S crystals. The absence of van Hove singularities or similar peaks in the electronic density of states of more than 3.000 candidate phases rules out conventional superconductivity in C-S-H at room-temperature.

Using a fast and accurate neural network potential we are able to systematically explore the energy landscape of large unit cells of bulk magnesium oxide with the minima hopping method. The potential is trained with a focus on the near-stoichiometric compositions, in particular on suboxides, i.e., Mg$_x$O$_{1-x}$ with $0.50

Atomic fingerprints are commonly used for the characterization of local environments of atoms in machine learning and other contexts. In this work, we study the behavior of two widely used fingerprints, namely the smooth overlap of atomic positions (SOAP) and the atom-centered symmetry functions (ACSF), under finite changes of atomic positions and demonstrate the existence of manifolds of quasi-constant fingerprints. These manifolds are found numerically by following eigenvectors of the sensitivity matrix with quasi-zero eigenvalues. The existence of such manifolds in ACSF and SOAP causes a failure to machine learn four-body interactions such as torsional energies that are part of standard force fields. No such manifolds can be found for the Overlap Matrix (OM) fingerprint due to its intrinsic many-body character., Comment: 8 pages, 5 figures

Efforts toward stabilization of the Si$_{20}$ fullerene through different schemes have failed despite several theoretical predictions. However, recently Tillmann {\it et. al.} succeeded to stabilize the Si$_{20}$ fullerene through exohedral decoration with eight Cl substituents and twelve SiCl$_3$ groups on the surface and enclosing Cl$^-$ ion. A deeper understanding on what factors lead to stabilization will open the path for stabilizing other systems of interest. Here, we employ the minima hopping method within density functional theory to understand the potential energy surface. The study shows that the exo-endo halide decoration of the cage alters the glassy nature of the potential energy surface of pure cage to structure seeker. Further analysis of different properties of the global minima, reveal that the extra electron instead of residing on the central encapsulated atom in the cage, it is distributed on the cage and increases the encapsulation energy; thereby stabilizing the system. We also provide estimates of the stability for different kind of exo-endo halide decorations and their feasible realization in experiments.

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations., Comment: 13 pages, 11 figures

Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work we present the simplex method which can perform the inverse operation, i.e. calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large data set of fingerprints, we can find a particular largest volume simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can by used in a fully automatic way to analyse structures. In this way we can for instance detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest volume simplex we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced., Comment: 10 pages, 7 figures

Using methods borrowed from machine learning we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy., Comment: 3 figures

Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called structural descriptors, have been proposed. In this work, we compare the performance of fingerprints based on the Overlap Matrix(OM), the Smooth Overlap of Atomic Positions (SOAP), Behler-Parrinello atom-centered symmetry functions (ACSF), modified Behler-Parrinello symmetry functions (MBSF) used in the ANI-1ccx potential and the Faber-Christensen-Huang-Lilienfeld (FCHL) fingerprint under various aspects. We study their ability to resolve differences in local environments and in particular examine whether there are certain atomic movements that leave the fingerprints exactly or nearly invariant. For this purpose, we introduce a sensitivity matrix whose eigenvalues quantify the effect of atomic displacement modes on the fingerprint. Further, we check whether these displacements correlate with the variation of localized physical quantities such as forces. Finally, we extend our examination to the correlation between molecular fingerprints obtained from the atomic fingerprints and global quantities of entire molecules., Comment: 18 pages, 11 figures

Monte Carlo simulations are a powerful tool to investigate the thermodynamic properties of atomic systems. In practice however, sampling of the complete configuration space is often hindered by high energy barriers between different regions of configuration space which can make ergodic sampling completely infeasible within accessible simulation times. Although several extensions to the conventional Monte Carlo scheme have been developed, that enable the treatment of such systems, these extensions often entail substantial computational cost or rely on the harmonic approximation. In this work we propose an exact method called Funnel Hopping Monte Carlo (FHMC) that is inspired by the the ideas of smart darting but is more efficient. Gaussian mixtures are used to approximate the Boltzmann distribution around local energy minima which are then used to propose high quality Monte Carlo moves that enable the Monte Carlo simulation to directly jump between different funnels. To fit the Gaussian mixtures we developed an extended version of the expectation-maximization algorithm that is able to take advantage of the high symmetry present in many low energy configurations. We demonstrate the methods performance on the example of the 38 as well as the 75 atom Lennard-Jones clusters which are well known for their double funnel energy landscapes that prevent ergodic sampling with conventional Monte Carlo simulations. By integrating FHMC into the parallel tempering scheme we were able to reduce the number of steps required until convergence of the simulation significantly., Comment: 12 pages, 6 figures

Finding complex reaction and transformation pathways, involving many intermediate states, is in general not possible on the DFT level with existing simulation methods due to the very large number of required energy and force evaluations. This is due to a large extent to the fact that for complex reactions, it is not possible to determine which atom in the educt is mapped onto which atom in the product. Trying out all possible atomic index mappings is not feasible because of the factorial increase in the number of possible mappings. By using a penalty function that is invariant under index permutations, we can bias the potential energy surface in such a way that it obtains the characteristics of a structure seeker whose global minimum is the product. By performing a Minima Hopping based global optimization on this biased potential energy surface we can rapidly find intermediate states that lead into the global minimum. Based on this information we can then extract the full reaction pathway. We first demonstrate for a benchmark system, namely LJ38 that our method allows to preferentially find intermediate states that are relevant for the lowest energy reaction pathway and that we, therefore, need a much smaller number of intermediate states than previous methods to find the lowest energy reaction pathway. Finally, we apply the method to two real systems, C60 and C20H20 and show that the found reaction pathway contains valuable information on how the system can be synthesized.

Continuum models to handle solvent and electrolyte effects in an effective way have a long tradition in quantum-chemistry simulations and are nowadays also being introduced in computational condensed-matter and materials simulations. A key ingredient of continuum models is the choice of the solute cavity, i.e. the definition of the sharp or smooth boundary between the regions of space occupied by the quantum-mechanical (QM) system and the continuum embedding environment. Although most of the solute-based approaches developed lead to models with comparable and high accuracy when applied to small organic molecules, they can introduce significant artifacts when complex systems are considered. As an example, condensed-matter simulations often deal with supports that present open structures. Similarly, unphysical pockets of continuum solvent may appear in systems featuring multiple molecular components. Here, we introduce a solvent-aware approach to eliminate the unphysical effects where regions of space smaller than the size of a single solvent molecule could still be filled with a continuum environment. We do this by defining a smoothly varying solute cavity that overcomes several of the limitations of straightforward solute-based definitions. This new approach applies to any smooth local definition of the continuum interface, being it based on the electronic density or the atomic positions of the QM system. It produces boundaries that are continuously differentiable with respect to the QM degrees of freedom, leading to accurate forces and/or Kohn-Sham potentials. Benchmarks on semiconductor substrates and on explicit water substrates confirm the flexibility and the accuracy of the approach and provide a general set of parameters for condensed-matter systems featuring open structures and/or explicit liquid components.

We perform a thorough structural search with the minima hopping method (MHM) to explore low-energy structures of methylammonium lead iodide. By combining the MHM with a forcefield, we efficiently screen vast portions of the configurational space with large simulation cells containing up to 96 atoms. Our search reveals two structures of methylammonium iodide perovskite (MAPI) that are substantially lower in energy than the well-studied experimentally observed low-temperature $Pnma$ orthorhombic phase according to density functional calculations. Both structures have not yet been reported in the literature for MAPI, but our results show that they could emerge as thermodynamically stable phases via compression at low temperatures. In terms of the electronic properties, the two phases exhibit larger band gaps than the standard perovskite-type structures. Hence, pressure induced phase selection at technologically achievable pressures (i.e., via thin-film strain) is a route towards the synthesis of several MAPI polymorph with variable band gaps.

A good hydrogen storage material should adsorb hydrogen in high concentrations and with optimal binding energies. Numerous mixed carbon boron fullerenes which are decorated with metal atoms were previously constructed by hand and proposed as a promising material in this context. We present a fully ab-initio, unbiased structure search in the configurational space of decorated C48B12 and find that most of the hitherto postulated ground state structures are not ground states. We determine the energetically lowest configurations for Be, Ca, Li and Sc decorated C48B12 clusters.

Performing high accuracy hybrid functional calculations for condensed matter systems containing a large number of atoms is at present computationally very demanding - when not out of reach - if high quality basis sets are used. We present a highly efficient multiple GPU implementation of the exact exchange operator which allows hybrid functional density-functional theory calculations with systematic basis sets without additional approximations for up to a thousand atoms. This method is implemented in a portable real-space-based algorithm, released as an open-source package. With such a framework hybrid DFT calculations of high quality become accessible on state-of-the-art supercomputers within a time-to-solution of the same order of magnitude as traditional semilocal-GGA functionals.

We report on the ab initio discovery of a novel putative ground state for quasi two-dimensional TiO$_2$ through a structural search using the minima hopping method with an artificial neural network potential. The structure is based on a honeycomb lattice and is energetically lower than the experimentally reported lepidocrocite sheet by 7~meV/atom, and merely 13~meV/atom higher in energy than the ground state rutile bulk structure. According to our calculations, the hexagonal sheet is stable against mechanical stress, it is chemically inert and can be deposited on various substrates without disrupting the structure. Its properties differ significantly from all known TiO$_2$ bulk phases with a large gap of 5.05~eV that can be tuned through strain engineering., Comment: 8 pages, 5 figures

Pressure-induced superconductivity and structural phase transitions in phosphorous (P) are studied by resistivity measurements under pressures up to 170 GPa and fully $ab-initio$ crystal structure and superconductivity calculations up to 350 GPa. Two distinct superconducting transition temperature (T$_{c}$) vs. pressure ($P$) trends at low pressure have been reported more than 30 years ago, and for the first time we are able to reproduce them and devise a consistent explanation founded on thermodynamically metastable phases of black-phosphorous. Our experimental and theoretical results form a single, consistent picture which not only provides a clear understanding of elemental P under pressure but also sheds light on the long-standing and unsolved $anomalous$ superconductivity trend. Moreover, at higher pressures we predict a similar scenario of multiple metastable structures which coexist beyond their thermodynamical stability range. Metastable phases of P experimentally accessible at pressures above 240 GPa should exhibit T$_{c}$'s as high as 15 K, i.e. three times larger than the predicted value for the ground-state crystal structure. We observe that all the metastable structures systematically exhibit larger transition temperatures than the ground-state ones, indicating that the exploration of metastable phases represents a promising route to design materials with improved superconducting properties., Comment: 14 pages, 4 figures

Using multiwavelets, we have obtained total energies and corresponding atomization energies for the GGA-PBE and hybrid-PBE0 density functionals for a test set of 211 molecules with an unprecedented and guaranteed $\mu$Hartree accuracy. These quasi-exact references allow us to quantify the accuracy of standard all-electron basis sets that are believed to be highly accurate for molecules, such as Gaussian-type orbitals (GTOs), all-electron numeric atom-centered orbitals (NAOs) and full-potential augmented plane wave (APW) methods. We show that NAOs are able to achieve the so-called chemical accuracy (1 kcal/mol) for the typical basis set sizes used in applications, for both total and atomization energies. For GTOs, a triple-zeta quality basis has mean errors of ~10kcal/mol in total energies, while chemical accuracy is almost reached for a quintuple-zeta basis. Due to systematic error cancellations, atomization energy errors are reduced by almost an order of magnitude, placing chemical accuracy within reach also for medium to large GTO bases, albeit with significant outliers. In order to check the accuracy of the computed densities, we have also investigated the dipole moments, where in general, only the largest NAO and GTO bases are able to yield errors below 0.01 Debye. The observed errors are similar across the different functionals considered here.

We investigate the possibility of achieving high-temperature superconductivity in hydrides under pressure by inducing metallization of otherwise insulating phases through doping, a path previously used to render standard semiconductors superconducting at ambient pressure. Following this idea, we study H$_2$O, one of the most abundant and well-studied substances, we identify nitrogen as the most likely and promising substitution/dopant. We show that for realistic levels of doping of a few percent, the phase X of ice becomes superconducting with a critical temperature of about 60 K at 150GPa. In view of the vast number of hydrides that are strongly covalent bonded, but that remain insulating until rather large pressures, our results open a series of new possibilities in the quest for novel high-temperature superconductors.

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows to understand important characteristics like thermodynamic, dynamic and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows to generate an approximate network of the minima, their connectivity and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.

Semiconducting half- and, to a lesser extent, full-Heusler compounds are promising thermoelectric materials due to their compelling electronic properties with large power factors. However, intrinsically high thermal conductivity resulting in a limited thermoelectric efficiency has so far impeded their widespread use in practical applications. Here, we report the computational discovery of a class of hitherto unknown stable semiconducting full-Heusler compounds with ten valence electrons ($X_2YZ$, $X$=Ca, Sr, and Ba; $Y$= Au and Hg; $Z$=Sn, Pb, As, Sb, and Bi) through high-throughput $ab-initio$ screening. These new compounds exhibit ultralow lattice thermal conductivity $\kappa_{\text{L}}$ close to the theoretical minimum due to strong anharmonic rattling of the heavy noble metals, while preserving high power factors, thus resulting in excellent phonon-glass electron-crystal materials., Comment: 6 pages, 5 figures

Hydrogen-rich compounds have been extensively studied both theoretically and experimentally in the quest for novel high-temperature superconductors. Reports on sulfur-hydride attaining metallicity under pressure and exhibiting superconductivity at temperatures as high as 200 K have spurred an intense search for room-temperature superconductors in hydride materials. Recently, compressed phosphine was reported to metallize at pressures above 45 GPa, reaching a superconducting transition temperature (T$_{c}$) of 100 K at 200 GPa. However, neither the exact composition nor the crystal structure of the superconducting phase have been conclusively determined. In this work the phase diagram of PH$_n$ ($n=1,2,3,4,5,6$) was extensively explored by means of {\it ab initio} crystal structure predictions using the Minima Hopping Method (MHM). The results do not support the existence of thermodynamically stable PH$_n$ compounds, which exhibit a tendency for elemental decomposition at high pressure even when vibrational contributions to the free energies are taken into account. Although the lowest energy phases of PH$_{1,2,3}$ display T$_{c}$'s comparable to experiments, it remains uncertain if the measured values of T$_{c}$ can be fully attributed to a phase-pure compound of PH$_n$.

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell we introduce crystal fingerprints that can be calculated easily and allow to define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method is an useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms and high-throughput screenings.

Silicon materials play a key role in many technologically relevant fields, ranging from the electronic to the photovoltaic industry. A systematic search for silicon allotropes was performed by employing a modified ab initio minima hopping crystal structure prediction method. The algorithm was optimized to specifically investigate the hitherto barely explored low-density regime of the silicon phase diagram by imitating the guest-host concept of clathrate compounds. In total 44 metastable phases are presented, of which 11 exhibit direct or quasi-direct band-gaps in the range of $\approx$1.0-1.8 eV, close to the optimal Shockley-Queisser limit of $\approx$1.4 eV, with a stronger overlap of the absorption spectra with the solar spectrum compared to conventional diamond silicon. Due to the structural resemblance to known clathrate compounds it is expected that the predicted phases can be synthesized.

We use ab initio global structural prediction, and specifically the minima hopping method, to explore the periodic table in search of novel oxide phases. In total, we study 183 different compositions of the form MXO2, where M=(Cu, Ag, Au) and X is an element of the periodic table. This set includes the well-known Cu delafossite compounds that are, up to now, the best p-type transparent conductive oxides known to mankind. Our calculations discover 81 stable compositions, out of which only 36 are included in available databases. Some of these new phases are potentially good candidates for transparent electrodes. These results demonstrate, on one hand, how incomplete is still our knowledge of the phase-space of stable ternary materials. On the other hand, we show that structural prediction combined with high-throughput approaches is a powerful tool to extend that knowledge, paving the way for the experimental discovery of new materials on a large scale.

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the total energy. This prevents for instance an accurate description of the energetics of systems where long range charge transfer is important as well as of ionized systems. We propose therefore not to target directly with machine learning methods the total energy but an intermediate physical quantity namely the charge density, which then in turn allows to determine the total energy. By allowing the electronic charge to distribute itself in an optimal way over the system, we can describe not only neutral but also ionized systems with unprecedented accuracy. We demonstrate the power of our approach for both neutral and ionized NaCl clusters where charge redistribution plays a decisive role for the energetics. We are able to obtain chemical accuracy, i.e. errors of less than a milli Hartree per atom compared to the reference density functional results. The introduction of physically motivated quantities which are determined by the short range atomic environment via a neural network leads also to an increased stability of the machine learning process and transferability of the potential., Comment: 4 figures

Density Functional Theory calculations traditionally suffer from an inherent cubic scaling with respect to the size of the system, making big calculations extremely expensive. This cubic scaling can be avoided by the use of so-called linear scaling algorithms, which have been developed during the last few decades. In this way it becomes possible to perform ab-initio calculations for several tens of thousands of atoms or even more within a reasonable time frame. However, even though the use of linear scaling algorithms is physically well justified, their implementation often introduces some small errors. Consequently most implementations offering such a linear complexity either yield only a limited accuracy or, if one wants to go beyond this restriction, require a tedious fine tuning of many parameters. In our linear scaling approach within the BigDFT package, we were able to overcome this restriction. Using an ansatz based on localized support functions expressed in an underlying Daubechies wavelet basis -- which offers ideal properties for accurate linear scaling calculations -- we obtain an amazingly high accuracy and a universal applicability while still keeping the possibility of simulating large systems with only a moderate demand of computing resources.

Optimizations of atomic positions belong to the most commonly performed tasks in electronic structure calculations. Many simulations like global minimum searches or characterizations of chemical reactions require performing hundreds or thousands of minimizations or saddle computations. To automatize these tasks, optimization algorithms must not only be efficient, but also very reliable. Unfortunately computational noise in forces and energies is inherent to electronic structure codes. This computational noise poses a sever problem to the stability of efficient optimization methods like the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm. We here present a technique that allows obtaining significant curvature information of noisy potential energy surfaces. We use this technique to construct both, a stabilized quasi-Newton minimization method and a stabilized quasi-Newton saddle finding approach. We demonstrate with the help of benchmarks that both the minimizer and the saddle finding approach are superior to comparable existing methods.

The Minima Hopping global optimization method uses physically realizable molecular dynamics moves in combination with an energy feedback that guarantees the escape from any potential energy funnel. For the purpose of finding reactions pathways, we argue that Minima Hopping is particularly suitable as a guide through the potential energy landscape and as a generator for pairs of minima that can be used as input structures for methods capable of finding transition states between two minima. For Lennard-Jones benchmark systems we compared this Minima Hopping guided path search method to a known approach for the exploration of potential energy landscapes that is based on deterministic mode-following. Although we used a stabilized mode-following technique that reliably allows to follow distinct directions when escaping from a local minimum, we observed that Minima Hopping guided path search is far superior in finding lowest-barrier reaction pathways. We therefore suggest that Minima Hopping guided path search can be used as a simple and efficient way to identify energetically low-lying chemical reaction pathways. Finally we applied the Minima Hopping guided path search approach to 75-atom and 102-atom Lennard Jones systems. For the 75-atom system we found pathways whose highest energies are significantly lower than the highest energy along the previously published lowest-barrier pathway. Furthermore, many of these pathways contain a smaller number of intermediate transition states than the previously publish lowest-barrier pathway. In case of the 102-atom system Minima Hopping guided path search found a previously unknown and energetically low-lying funnel., Comment: 15 pages, 8 figures

We demonstrate that Daubechies wavelets can be used to construct a minimal set of optimized localized contracted basis functions in which the Kohn-Sham orbitals can be represented with an arbitrarily high, controllable precision. Ground state energies and the forces acting on the ions can be calculated in this basis with the same accuracy as if they were calculated directly in a Daubechies wavelets basis, provided that the amplitude of these contracted basis functions is sufficiently small on the surface of the localization region, which is guaranteed by the optimization procedure described in this work. This approach reduces the computational costs of DFT calculations, and can be combined with sparse matrix algebra to obtain linear scaling with respect to the number of electrons in the system. Calculations on systems of 10,000 atoms or more thus become feasible in a systematic basis set with moderate computational resources. Further computational savings can be achieved by exploiting the similarity of the contracted basis functions for closely related environments, e.g. in geometry optimizations or combined calculations of neutral and charged systems.

Based on a recently introduced metric for measuring distances between configurations, we in- troduce distance-energy (DE) plots to characterize the potential energy surface (PES) of clusters. Producing such plots is computationally feasible on the density functional (DFT) level since it re- quires only a set of a few hundred stable low energy configurations including the global minimum. By comparison with standard criteria based on disconnectivity graphs and on the dynamics of Lennard- Jones clusters we show that the DE plots convey the necessary information about the character of the potential energy surface and allow to distinguish between glassy and non-glassy systems. We then apply this analysis to real systems on the DFT level and show that both glassy and non-glassy clusters can be found in simulations. It however turns out that among our investigated clusters only those can be synthesized experimentally which exhibit a non-glassy landscape.

Systematic ab initio structure prediction was applied for the first time to predict low energy surface reconstructions by employing the minima hopping method on the \alpha-boron (111) surface. Novel reconstruction geometries were identified and carefully characterized in terms of structural and electronic properties. Our calculations predict the formation of a planar, mono-layer sheet at the surface, which is responsible for conductive surface states. Furthermore, the isolated boron sheet is shown to be the ground state 2D-structure in vacuum at a hole density of \eta=1/5 and is therefore a potential candidate as a precursor for boron nano-structures.

According to previous interpretations of experimental data, sodium-scandium double-cation borohydride NaSc(BH$_4$)$_4$ crystallizes in the crystallographic space group $Cmcm$ where each sodium (scandium) atom is surrounded by six scandium (sodium) atoms. A careful investigation of this phase based on \textit{ab initio} calculations indicates that the structure is dynamically unstable and gives rise to an energetically and dynamically more favorable phase with $C222_1$ symmetry and nearly identical x-ray diffraction pattern. By additionally performing extensive structural searches with the minima-hopping method we discover a class of new low-energy structures exhibiting a novel structural motif in which each sodium (scandium) atom is surrounded by four scandium (sodium) atoms arranged at the corners of either a rectangle with nearly equal sides or a tetrahedron. These new phases are all predicted to be insulators with band gaps of $7.9-8.2$ eV. Finally, we estimate the influence of these structures on the hydrogen-storage performance of NaSc(BH$_4$)$_4$., Comment: Version published

We present novel structural motifs for boron-carbon nano-cages of the stochiometries B12C48 and B12C50, based on first principle calculations. These configurations are distinct from those proposed so far by the fact that the boron atoms are not isolated and distributed over the entire surface of the cages, but rather aggregate at one location to form a patch. Our putative ground state of B12C48 is 1.8 eV lower in energy than the previously proposed ground state and violates all the suggested empirical rules for constructing low energy fullerenes. The B12C50 configuration is energetically even more favorable than B12C48, showing that structures derived from the C60 Buckminsterfullerene are not necessarily magic sizes for heterofullerenes.

We study the thermodynamic stability at low temperatures of a series of alkali metal/zinc double-cation borohydrides, including LiZn(BH$_4$)$_3$, LiZn$_2$(BH$_4$)$_5$, NaZn(BH$_4$)$_3$, NaZn$_2$(BH$_4$)$_5$, KZn(BH$_4$)$_3$, and KZn$_2$(BH$_4$)$_5$. While LiZn$_2$(BH$_4$)$_5$, NaZn(BH$_4$)$_3$, NaZn$_2$(BH$_4$)$_5$ and KZn(BH$_4$)$_3$ were recently synthesized, LiZn(BH$_4$)$_3$ and KZn$_2$(BH$_4$)$_5$ are hypothetical compounds. Using the minima-hopping method, we discover two new lowest-energy structures for NaZn(BH$_4$)$_3$ and KZn$_2$(BH$_4$)$_5$ which belong to the $C2/c$ and $P2$ space groups, respectively. These structures are predicted to be both thermodynamically stable and dynamically stable, implying that their existence may be possible. On the other hand, the lowest-energy $P1$ structure of LiZn(BH$_4$)$_3$ is predicted to be unstable, suggesting a possible reason elucidating why this compound has not been experimentally identified. In exploring the low-energy structures of these compounds, we find that their energetic ordering is sensitive to the inclusion of the van der Waals interactions. We also find that a proper treatment of these interactions, e.g., as given by a non-local density functional such as vdW-DF2, is necessary to address the stability of the low-energy structures of these compounds., Comment: Final version

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations.