Search

Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to properties such as room temperature ferroelectricity, low-energy synthesis, malleability, and potential for multiaxial polarization. The ferroelectric molecular salt HdabcoClO 4 is of particular interest due to its ultrafast ferroelectric room-temperature switching. However, so far, there is limited understanding of the nature of dynamical disorder arising in these compounds. Here, we employ the neural network NeuralIL to train a machine-learned force field (MLFF) with training data generated using density functional theory. The resulting MLFF-MD simulations exhibit phase transitions and thermal expansion in line with earlier reported experimental results, for both a low-temperature phase transition coinciding with the orientational disorder of ClO 4 - and the onset of rotation of both Hdabco + and ClO 4 - in a high-temperature phase transition. We also find proton transfer even in the low-temperature phase, which increases with temperature and leads to associated proton disorder as well as the onset of disorder in the direction of the hydrogen-bonded chains., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Determining the local coordination of the active site is a prerequisite for the reliable modeling of single-atom catalysts (SACs). Obtaining such information is difficult on powder-based systems and much emphasis is placed on density functional theory computations based on idealized low-index surfaces of the support. In this work, we investigate how Pt atoms bind to the (11̅02) facet of α-Fe 2 O 3 ; a common support material in SACs. Using a combination of scanning tunneling microscopy, X-ray photoelectron spectroscopy, and an extensive computational evolutionary search, we find that Pt atoms significantly reconfigure the support lattice to facilitate a pseudolinear coordination to surface oxygen atoms. Despite breaking three surface Fe-O bonds, this geometry is favored by 0.84 eV over the best configuration involving an unperturbed support. We suggest that the linear O-Pt-O configuration is common in reactive Pt-based SAC systems because it balances thermal stability with the ability to adsorb reactants from the gas phase. Moreover, we conclude that extensive structural searches are necessary to determine realistic active site geometries in single-atom catalysis.

The investigation of inhomogeneous surfaces, where various local structures coexist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × m ) Ti-rich surfaces at (110)-oriented SrTiO 3 by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the inhomogeneous SrTiO 3 (110)-(2 × m ) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. The approach could be extended to other complex metal oxides featuring large coexisting surface reconstructions., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted on the data set composition itself. The reliable identification of erroneously predicted configurations to extend a given data set is therefore of high priority. Yet, uncertainty estimation techniques have achieved mixed results for machine learning potentials. Consequently, a general and versatile method to correlate energy or atomic force uncertainties with the model error has remained elusive to date. In the current work, we show that epistemic uncertainty cannot correlate with model error by definition but can be aggregated over groups of atoms to yield a strong correlation. We demonstrate that our method correctly estimates prediction errors both globally per structure and locally resolved per atom. The direct correlation of local uncertainty and local error is used to design an active learning framework based on identifying local subregions of a large simulation cell and performing ab initio calculations only for the subregion subsequently. We successfully utilized this method to perform active learning in the low-data regime for liquid water.

We explore the phonon transport properties of defect-laden bilayer PtSTe using equilibrium molecular dynamics simulations based on a neural-network force field. Defects prove very efficient at depressing the thermal conductivity of the structure, and flower defects have a particularly powerful effect, comparable to that of double vacancies. Furthermore, the conductivity of the structure with flower defects exhibits an unusual temperature dependence due to structural instability at high temperatures. We look into the distortion to normal modes around the defect by means of the projected phonon density of states and find diverse phenomena including localized modes and blue shifts., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Recent theoretical and experimental research suggests that θ-TaN is a semimetal with high thermal conductivity (κ), primarily due to the contribution of phonons (κ_{ph}). By using first-principles calculations, we show a nonmonotonic pressure dependence of the κ of θ-TaN. κ_{ph} first increases until it reaches a maximum at around 60 GPa, and then decreases. This anomalous behavior is a consequence of the competing pressure responses of phonon-phonon and phonon-electron interactions, in contrast to the known materials BAs and BP, where the nonmonotonic pressure dependence is caused by the interplay between different phonon-phonon scattering channels. Although TaN has phonon dispersion features similar to BAs at ambient pressure, its response to pressure is different and an overall stiffening of the phonon branches takes place. Consequently, the relevant phonon-phonon scattering weakens as pressure increases. However, the increased electronic density of states near the Fermi level, and specifically the emergence of additional pockets of the Fermi surface at the high-symmetry L point in the Brillouin zone, leads to a substantial increase in phonon-electron scattering at high pressures, driving a decrease in κ_{ph}. At intermediate pressures (∼20-70  GPa), the κ of TaN surpasses that of BAs. Our Letter provides deeper insight into phonon transport in semimetals and metals where phonon-electron scattering is relevant.

Transition metal dichalcogenides are investigated for various applications at the nanoscale because of their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the same time, these materials often contain relatively large amounts of point defects. Here, we provide a systematic analysis of the impact of intrinsic and selected extrinsic defects on the lattice thermal conductivity of MoS 2 and WS 2 monolayers. We combine Boltzmann transport theory and Green's function-based T -matrix approach for the calculation of scattering rates. The force constants for the defect configurations are obtained from density functional theory calculations via a regression approach, which allows us to sample a rather large number of defects at a moderate computational cost and to systematically enforce both the translational and rotational acoustic sum rules. The calculated lattice thermal conductivity is in quantitative agreement with the experimental data for heat transport and defect concentrations for both MoS 2 and WS 2 . Crucially, this demonstrates that the strong deviation from a 1/ T temperature dependence of the lattice thermal conductivity observed experimentally can be fully explained by the presence of point defects. We furthermore predict the scattering strengths of the intrinsic defects to decrease in the sequence V Mo ≈ V 2S = > V 2S ⊥ > V S > S ad in both materials, while the scattering rates for the extrinsic (adatom) defects decrease with increasing mass such that Li ad > Na ad > K ad . Compared with earlier work, we find that both intrinsic and extrinsic adatoms are relatively weak scatterers. We attribute this difference to the treatment of the translational and rotational acoustic sum rules, which, if not enforced, can lead to spurious contributions in the zero-frequency limit., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

One of the well-known limitations of Kohn-Sham density functional theory is the tendency to strongly underestimate bandgaps. Meta-generalized gradient approximations (mGGAs), which include the kinetic energy density in the functional form, have been shown to significantly alleviate this deficiency. In this study, we explore the mechanisms responsible for this improvement from the angle of the underlying local densities. We find that the highest occupied and lowest unoccupied states are distinct in the space of the underlying descriptors. The gap opening is compared to a simple scaling of the local density approximation, and two mechanisms responsible for opening the mGGA gaps are identified. First of all, the relatively large negative derivative of the functional form with respect to reduced kinetic energy tends to elevate the lowest unoccupied state. Second, the curvature of functional, which ensures that it is bounded, tends to lower the highest occupied state. Remarkably, these two mechanisms are found to be transferable over a large and diverse database of compounds., (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here, we present a generalization of the deep-ensemble design based on multiheaded neural networks and a heteroscedastic loss. It can efficiently deal with uncertainties in both energy and forces and take sources of aleatoric uncertainty affecting the training data into account. We compare uncertainty metrics based on deep ensembles, committees, and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer., (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

The space of generalized gradient approximation (GGA) and meta-GGA (mGGA) exchange approximations is systematically explored by training 25 new functionals to produce accurate lattice parameter, cohesive energy, and bandgap predictions. The trained functionals are used to reproduce exact constraints in a data-driven way and to understand the accuracy trade-off between the mentioned properties. The functionals are compared to notable mGGA functionals to analyze how changes in the enhancement factor maps influence the accuracy of predictions. Some of the trained functionals are found to perform on par with specialized functionals for bandgaps, while outperforming them on the other two properties. The error surface of our trained functionals can serve as a soft-limit of what mGGA functionals can achieve.

The determination of atomic structures in surface reconstructions has typically relied on structural models derived from intuition and domain knowledge. Evolutionary algorithms have emerged as powerful tools for such structure searches. However, when density functional theory is used to evaluate the energy the computational cost of a thorough exploration of the potential energy landscape is prohibitive. Here, we drive the exploration of the rich phase diagram of TiO x overlayer structures on SrTiO 3 (110) by combining the covariance matrix adaptation evolution strategy (CMA-ES) and a neural-network force field (NNFF) as a surrogate energy model. By training solely on SrTiO 3 (110) 4×1 overlayer structures and performing CMA-ES runs on 3×1, 4×1 and 5×1 overlayers, we verify the transferability of the NNFF. The speedup due to the surrogate model allows taking advantage of the stochastic nature of the CMA-ES to perform exhaustive sets of explorations and identify both known and new low-energy reconstructions., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Benchmarking DFT functionals is complicated since the results highly depend on which properties and materials were used in the process. Unwanted biases can be introduced if a data set contains too many examples of very similar materials. We show that a clustering based on the distribution of density gradient and kinetic energy density is able to identify groups of chemically distinct solids. We then propose a method to create smaller data sets or rebalance existing data sets in a way that no region of the meta-GGA descriptor space is overrepresented, yet the new data set reproduces average errors of the original set as closely as possible. We apply the method to an existing set of 44 inorganic solids and suggest a representative set of seven solids. The representative sets generated with this method can be used to make more general benchmarks or to train new functionals.

We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost. Built on the basis of a multilayer perceptron and spherical Bessel descriptors of the atomic environments, NeuralIL is implemented in such a way as to be fully automatically differentiable. It can thus be trained on ab initio forces instead of just energies, to make the most out of the available data, and can efficiently predict arbitrary derivatives of the potential energy. Using ethylammonium nitrate as the test system, we obtain out-of-sample accuracies better than 2 meV atom -1 (<0.05 kcal mol -1 ) in the energies and 70 meV Å -1 in the forces. We show that encoding the element-specific density in the spherical Bessel descriptors is key to achieving this. Harnessing the information provided by the forces drastically reduces the amount of atomic configurations required to train a neural network force field based on atom-centered descriptors. We choose the Swish-1 activation function and discuss the role of this choice in keeping the neural network differentiable. Furthermore, the possibility of training on small data sets allows for an ensemble-learning approach to the detection of extrapolation. Finally, we find that a separate treatment of long-range interactions is not required to achieve a high-quality representation of the potential energy surface of these dense ionic systems.

Molecular dynamics simulations are performed to characterize the nucleation behavior of organic compounds in the gas phase. Six basic molecular species are considered-ethylene, propylene, toluene, styrene, ethylbenzene, and para -xylene-in interaction with onion-like carbon nanostructures that model soot nanoparticles (NPs) at room temperature. We identify a shell-to-island aggregation process during the physisorption of aromatic molecules on the soot surface: The molecules tend to first cover the NP in a shell, on top of which additional adsorbates form island-shaped aggregates. We present results for the binding energy, suggesting that the NPs lead to the formation of more stable molecular aggregates in comparison with the pure gas phase. Our findings describe a plausible microscopic mechanism for the active role of soot in the formation and growth of organic particulate matter., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

The ever-growing interest in sustainable energy sources leads to a search for an efficient, stable, and inexpensive homogeneous water oxidation catalyst (WOC). Herein, the PO 4 3- templated synthesis of three abundant-metal-based germanotungstate (GT) clusters Na 15 [Ge 4 PCo 4 (H 2 O) 2 W 24 O 94 ] ⋅ 38H 2 O (Co 4 ), Na 2.5 K 17.5 [Ge 3 PCo 9 (OH) 5 (H 2 O) 4 W 30 O 115 ] ⋅ 45H 2 O (Co 9 ), Na 6 K 16 [Ge 4 P 4 Co 20 (OH) 14 (H 2 O) 18 W 36 O 150 ] ⋅ 61H 2 O (Co 20 ) with non-, quasi-, or full cubane motifs structurally strongly reminiscent of the naturally occurring {Mn 4 Ca} oxygen evolving complex (OEC) in photosystem II was achieved. Under the conditions tested, all three GT-scaffolds were active molecular WOCs, with Co 9 and Co 20 outperforming the well-known Na 10 [Co 4 (H 2 O) 2 (PW 9 O 34 ) 2 ] {Co 4 P 2 W 18 } by a factor of 2 as shown by a direct comparison of their turnover numbers (TONs). With TONs up to 159.9 and a turnover frequency of 0.608 s -1 Co 9 currently represents the fastest Co-GT-based WOC, and photoluminescence emission spectroscopy provided insights into its photocatalytic WOC mechanism. Cyclic voltammetry, dynamic light scattering, UV/Vis and IR spectroscopy showed recyclability and integrity of the catalysts under the applied conditions. The experimental results were supported by computational studies, which highlighted that the facilitated oxidation of Co 9 was due to the higher energy of its highest occupied molecular orbital electrons as compared to Co 4 ., (© 2021 The Authors. ChemSusChem published by Wiley-VCH GmbH.)

Extracting long-lasting performance from electronic devices and improving their reliability through effective heat management requires good thermal conductors. Taking both three- and four-phonon scattering as well as electron-phonon and isotope scattering into account, we predict that semimetallic θ-phase tantalum nitride (θ-TaN) has an ultrahigh thermal conductivity (κ), of 995 and 820  W m^{-1} K^{-1} at room temperature along the a and c axes, respectively. Phonons are found to be the main heat carriers, and the high κ hinges on a particular combination of factors: weak electron-phonon scattering, low isotopic mass disorder, and a large frequency gap between acoustic and optical phonon modes that, together with acoustic bunching, impedes three-phonon processes. On the other hand, four-phonon scattering is found to be significant. This study provides new insight into heat conduction in semimetallic solids and extends the search for high-κ materials into the realms of semimetals and noncubic crystal structures.

The WIEN2k program is based on the augmented plane wave plus local orbitals (APW+lo) method to solve the Kohn-Sham equations of density functional theory. The APW+lo method, which considers all electrons (core and valence) self-consistently in a full-potential treatment, is implemented very efficiently in WIEN2k, since various types of parallelization are available and many optimized numerical libraries can be used. Many properties can be calculated, ranging from the basic ones, such as the electronic band structure or the optimized atomic structure, to more specialized ones such as the nuclear magnetic resonance shielding tensor or the electric polarization. After a brief presentation of the APW+lo method, we review the usage, capabilities, and features of WIEN2k (version 19) in detail. The various options, properties, and available approximations for the exchange-correlation functional, as well as the external libraries or programs that can be used with WIEN2k, are mentioned. References to relevant applications and some examples are also given.

Understanding native point defects is fundamental in order to comprehend the properties of TiO 2 anatase in technological applications. The previous first-principles reports of defect-relevant quantities, such as formation energies and charge transition levels, are, however, scattered over a wide range. We perform a comparative study employing different approaches based on semilocal with Hubbard correction (DFT+U) and screened hybrid functionals in order to investigate the dependence defect properties on the employed computational method. While the defects in TiO 2 anatase, as in most transition-metal oxides, generally induce the localization of electrons or holes on atomic sites, we notice that, provided an alignment of the valence bands has been performed, the calculated defect formation energies and transition levels using semilocal functionals are in a fair agreement with those obtained using hybrid functionals. A similar conclusion can be reached for the thermochemistry of the Ti-O system and the limit values of the elemental chemical potentials. We interpret this as a cancellation of error between the self-interaction error and the overbinding of the O 2 molecule in semilocal functionals. Inclusion of a U term in the electron Hamiltonian offers a convenient way for obtaining more precise geometric and electronic configurations of the defective systems.

We combine state-of-the-art Green's-function methods and nonequilibrium molecular dynamics calculations to study phonon transport across the unconventional interfaces that make up crystal-phase and twinning superlattices in nanowires. We focus on two of their most paradigmatic building blocks: cubic (diamond/zinc blende) and hexagonal (lonsdaleite/wurtzite) polytypes of the same group-IV or III-V material. Specifically, we consider InP, GaP and Si, and both the twin boundaries between rotated cubic segments and the crystal-phase boundaries between different phases. We reveal the atomic-scale mechanisms that give rise to phonon scattering in these interfaces, quantify their thermal boundary resistance and illustrate the failure of common phenomenological models in predicting those features. In particular, we show that twin boundaries have a small but finite interface thermal resistance that can only be understood in terms of a fully atomistic picture.

The SCAN meta-generalized gradient approximation (GGA) functional is known to describe multiple properties of various materials with different types of bonds with greater accuracy, compared to the widely used PBE GGA functional. Yet, for alkali metals, SCAN shows worse agreement with experimental results than PBE despite using more information about the system. In the current study, this behavior for alkali metals is explained by identifying an inner semicore region which, within SCAN, contributes to an underbinding. The inner semicore push toward larger lattice constants is a general feature but is particularly important for very soft materials, such as the alkali metals, while for harder materials the valence region dominates.

We propose a convenient method to characterize the acoustic phonon branches of 2D monolayer materials using measurements of the infrared- and Raman-active vibrational modes of nanotubes. The relations we employ are derived from a symmetry analysis based directly on the line groups of nanotubes. We perform extensive ab initio calculations for the MoS2 monolayer and nanotubes to evaluate the method and illustrate all our results. Specifically, we show how the low-energy phonon transmission, a determining factor in thermal transport, can be easily and successfully reconstructed by this procedure.

Crystal imperfections such as dislocations strongly influence the performance and thermal transport behavior of GaN-based devices. We show that the experimental data used to parametrize the effect of dislocations on the thermal conductivity can be explained using only the reported film thickness and point defect concentrations. The analysis highlights the boundary-scattering-governed reduction of thermal conductivity in GaN, which had been underestimated in earlier models. To quantify the influence of dislocations on the thermal transport in GaN, we adopt a Green's function approach based on accurate ab initio interatomic force constants. While calculations at the level of density functional theory are necessary for three-phonon and point defect scattering, we show that scattering due to dislocations can be satisfactorily approximated using semiempirical potentials. This makes the Green's function approach to dislocation scattering a quantitatively predictive, yet computationally practical, method for obtaining detailed phonon scattering rates.

Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF 3 reaches a mean absolute error of 0.17 eV/Å 2 for the interatomic force constants, and it is less expensive than training the complete force field for such compounds. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.

A recent study of Mejia-Rodriguez and Trickey [Phys. Rev. A 96 , 052512 (2017)] showed that the deorbitalization procedure (replacing the exact Kohn-Sham kinetic-energy density by an approximate orbital-free expression) applied to exchange-correlation functionals of the meta-generalized gradient approximation (MGGA) can lead to important changes in the results for molecular properties. For the present work, the deorbitalization of MGGA functionals is further investigated by considering various properties of solids. It is shown that depending on the MGGA, common orbital-free approximations to the kinetic-energy density can be sufficiently accurate for the lattice constant, bulk modulus, and cohesive energy. For the bandgap, calculated with the modified Becke-Johnson MGGA potential, the deorbitalization has a larger impact on the results.

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71 178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high-throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.

We use ab initio calculations to predict the thermal conductivity of cubic SiC with different types of defects. An excellent quantitative agreement with previous experimental measurements is found. The results unveil that B&#95;{C} substitution has a much stronger effect than any of the other defect types in 3C-SiC, including vacancies. This finding contradicts the prediction of the classical mass-difference model of impurity scattering, according to which the effects of B&#95;{C} and N&#95;{C} would be similar and much smaller than that of the C vacancy. The strikingly different behavior of the B&#95;{C} defect arises from a unique pattern of resonant phonon scattering caused by the broken structural symmetry around the B impurity.