Ab Initio Quality Research Articles

Efficient machine learning approach for accurate free-energy profiles and kinetic rates.

The computational exploration of reactive processes is challenging due to the requirement of thorough sampling across the free energy landscape using accurate ab initio methods. To address these constraints, machine learning potentials are employed, yet their training for this kind of problem is still a laborious and tedious task. In this study, we present an efficient approach to train these potentials by cleverly using a single batch of unbiased trajectories that avoid the pitfalls of trajectories artificially biased along a suboptimal collective variable. This strategy, when integrated with current enhanced sampling techniques, allows to obtain free energy profiles and kinetic rates of ab initio quality, yet dramatically reducing the computational cost.

Systematic assessment of various universal machine‐learning interatomic potentials

AbstractMachine‐learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of ab initio quality over very large time and length scales. More recently, various universal machine‐learning models have been proposed as an out‐of‐box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate four different universal machine‐learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density‐functional‐theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine‐learning methodologies in materials science.

Mechanism of Antiferroelectricity in Polycrystalline ZrO2

AbstractThe size and electric field dependent induction of polarization in antiferroelectric ZrO2 is the key to several technological applications that are unimaginable a decade ago. However, the lack of a deeper understanding of the mechanism hinders progress. Molecular dynamics simulations of polycrystalline ZrO2, based on machine‐learned interatomic forces with near ab initio quality, shed light on the fundamental mechanism of the size effect on the transition fields. Stress in the oxygen sublattice is the most important factor. The so constructed interatomic forces allow the calculation of the transition fields as a function of the ZrO2 film thickness and predict the ferroelectricity at large thickness. The simulation results are validated with electrical and piezo response force microscopy measurements. The results allow a clear interpretation of the properties of the double‐hysteresis loops as well as the construction of the free energy landscape of ZrO2 grains.

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Hydrogen Diffusion in the Lower Mantle Revealed by Machine Learning Potentials

AbstractHydrogen may be incorporated into nominally anhydrous minerals including bridgmanite and post‐perovskite as defects, making the Earth's deep mantle a potentially significant water reservoir. The diffusion of hydrogen and its contribution to the electrical conductivity in the lower mantle are rarely explored and remain largely unconstrained. Here we calculate hydrogen diffusivity in hydrous bridgmanite and post‐perovskite, using molecular dynamics simulations driven by machine learning potentials of ab initio quality. Our findings reveal that hydrogen diffusivity significantly increases with increasing temperature and decreasing pressure, and is considerably sensitive to hydrogen incorporation mechanism. Among the four defect mechanisms examined, (Mg + 2H)Si and (Al + H)Si show similar patterns and yield the highest hydrogen diffusivity. Hydrogen diffusion is generally faster in post‐perovskite than in bridgmanite, and these two phases exhibit distinct diffusion anisotropies. Overall, hydrogen diffusion is slow on geological time scales and may result in heterogeneous water distribution in the lower mantle. Additionally, the proton conductivity of bridgmanite for (Mg + 2H)Si and (Al + H)Si defects aligns with the same order of magnitude of lower mantle conductivity, suggesting that the water distribution in the lower mantle may be inferred by examining the heterogeneity of electrical conductivity.

Thermal Conductivity of MgSiO3‐H2O System Determined by Machine Learning Potentials

AbstractThermal conductivity plays a pivotal role in understanding the dynamics and evolution of Earth's interior. The Earth's lower mantle is dominated by MgSiO3 polymorphs which may incorporate trace amounts of water. However, the thermal conductivity of MgSiO3‐H2O binary system remains poorly understood. Here, we calculate the thermal conductivity of water‐free and water‐bearing bridgmanite, post‐perovskite, and MgSiO3 melt, using a combination of Green‐Kubo method with molecular dynamics simulations based on a machine learning potential of ab initio quality. The thermal conductivities of water‐free bridgmanite and post‐perovskite overall agree well with previous theoretical and experimental studies. The presence of water mildly reduces the thermal conductivity of the host minerals, significantly weakens the temperature dependence of the thermal conductivity, and reduces the thermal anisotropy of post‐perovskite. Overall, water reduces the thermal conductivity difference between bridgmanite and post‐perovskite, and thus may attenuate lateral heterogeneities of the core‐mantle boundary heat flux.

Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks

Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a meaningful theoretical description of many of their properties accurate and computationally highly efficient methods are in high demand. These would avoid compromises regarding either the quality of modelling results or the level of complexity of the calculated properties. With the advent of machine learning approaches, it is now possible to generate such approaches with relatively little human effort. Here, we build on existing types of machine-learned force fields belonging to the moment-tensor and kernel-based potential families to develop a recipe for their efficient parametrization. This yields exceptionally accurate and computationally highly efficient force fields. The parametrization relies on reference configurations generated during molecular dynamics based, active learning runs. The performance of the potentials is benchmarked for a representative selection of commonly studied MOFs revealing a close to DFT accuracy in predicting forces and structural parameters for a set of validation structures. The same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a very high degree of computational efficiency. The exceptional accuracy of the parameterized force field potentials combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level.

Phase Stability of Large-Size Nanoparticle Alloy Catalysts at Ab Initio Quality Using a Nearsighted Force-Training Approach

Phase Stability of Large-Size Nanoparticle Alloy Catalysts at Ab Initio Quality Using a Nearsighted Force-Training Approach

Order-Disorder Phase Transition and Ionic Conductivity in a Li2B12H12 Solid Electrolyte.

Temperature-induced phase transitions and ionic conductivities of Li2B12H12 and LiCB11H12 were simulated with the use of machine learning interatomic potentials based on van der Waals-corrected density functional theory (rev-vdW-DF2 functional). The simulated temperature of order-disorder phase transition, lattice parameters, diffusion, ionic conductivity, and activation energies are in good agreement with experimental data. Our simulations of Li2B12H12 uncover the importance of the reorientational motion of the [B12H12]2- anion. In the ordered α-phase (T < 625 K), these anions have well-defined orientations, while in the disordered β-phase (T > 625 K), their orientations are random. In vacancy-rich systems, its complete rotation was observed, while in the ideal crystal, the anions display limited vabrational motion, indicating the static nature of the phase transition without dynamic disordering. The use of machine learning interatomic potentials has allowed us to study large systems (>2000 atoms) in long (nanosecond-scale) molecular dynamics runs with ab initio quality.

Anisotropic Collective Variables with Machine Learning Potential for Ab Initio Crystallization of Complex Ceramics.

Enhanced sampling molecular dynamics (MD) simulations have been extensively used in the phase transition study of simple crystalline materials, such as aluminum, silica, and ice. However, MD simulation of the crystallization process for complex crystalline materials still faces a formidable challenge due to their multicomponent induced multiphase problem. Here, we realize the ab initio accuracy MD crystallization simulations of complex ceramics by using anisotropic collective variables (CVs) and machine learning (ML) potential. The anisotropic X-ray diffraction intensity CVs provide precise identification of complex crystal structures with detailed crystallography information, while the ML potential makes it feasible to further perform enhanced sampling simulations with ab initio accuracy. We verify the universality and accuracy of this method through complex ceramics with three kinds of representative structures, i.e., Ti3SiC2 for the MAX structure, zircon for the mineral structure, and lead zirconate titanate for the perovskite structure. It demonstrates exceptional efficiency and ab initio quality in achieving crystallization and generating free energy surfaces of all these ceramics, facilitating the analysis and design of complex crystalline materials.

Recent advances in quantum fragmentation approaches to complex molecular and condensed‐phase systems

AbstractQuantum mechanical (QM) calculations are critical in quantitatively understanding the relationship between the structure and physicochemical properties of various chemical systems. However, the sharply increasing computational cost with the system size has severely hindered applying direct QM calculations on large‐sized systems. Hence, linear‐scaling and/or fragmentation QM methods have been proposed to overcome this difficulty. In this review, we focus on the recent development and applications of the electrostatically embedded generalized molecular fractionation with the conjugate caps (EE‐GMFCC) method in probing various properties of complex large molecules and condensed‐phase systems. The EE‐GMFCC method is now capable of describing the localized excited states of biomolecules and molecular crystals with a chromophore. The EE‐GMF method is also combined with anharmonic vibrational calculations for accurate simulation of the infrared spectrum of the magic number H+(H2O)21 cluster at the coupled cluster level. With an adaptive fragmentation scheme, the EE‐GMF‐based ab initio molecular dynamics is able to directly simulate chemical reactions occurred in atmospheric molecular clusters. Furthermore, by combining the EE‐GMF(CC) method and deep machine learning techniques, neural network potentials can be efficiently constructed for accurate simulations of complex systems with the accuracy of high‐level wave function methods. The EE‐GMF(CC) method is expected to become a practical tool for quantitative description of complex large molecules and condensed‐phase systems with high‐level ab initio theories or ab initio quality potentials.This article is categorized under: Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods Structure and Mechanism &gt; Computational Biochemistry and Biophysics Structure and Mechanism &gt; Reaction Mechanisms and Catalysis

Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution.

The study of the thermodynamics, kinetics, and microscopic mechanisms of chemical reactions in solution requires the use of advanced free-energy methods for predictions to be quantitative. This task is however a formidable one for atomistic simulation methods, as the cost of quantum-based ab initio approaches, to obtain statistically meaningful samplings of the relevant chemical spaces and networks, becomes exceedingly heavy. In this work, we critically assess the optimal structure and minimal size of an ab initio training set able to lead to accurate free-energy profiles sampled with neural network potentials. The results allow one to propose an ab initio protocol where the ad hoc inclusion of a machine-learning (ML)-based task can significantly increase the computational efficiency, while keeping the ab initio accuracy and, at the same time, avoiding some of the notorious extrapolation risks in typical atomistic ML approaches. We focus on two representative, and computationally challenging, reaction steps of the classic Strecker-cyanohydrin mechanism for glycine synthesis in water solution, where the main precursors are formaldehyde and hydrogen cyanide. We demonstrate that indistinguishable ab initio quality results are obtained, thanks to the ML subprotocol, at about 1 order of magnitude less of computational load.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform.

Nonadiabatic (NA) molecular dynamics (MD) allows one to investigate far-from-equilibrium processes in nanoscale and molecular materials at the atomistic level and in the time domain, mimicking time-resolved spectroscopic experiments. Ab initio NAMD is limited to about 100 atoms and a few picoseconds, due to computational cost of excitation energies and NA couplings. We develop a straightforward methodology that can extend ab initio quality NAMD to nanoseconds and thousands of atoms. The ab initio NAMD Hamiltonian is sampled and interpolated along a trajectory using a Fourier transform, and then, it is used to perform NAMD with known algorithms. The methodology relies on the classical path approximation, which holds for many materials and processes. To achieve a complete ab initio quality description, the trajectory can be obtained using an ab initio trained machine learning force field. The method is demonstrated with charge carrier trapping and relaxation in hybrid organic-inorganic and all-inorganic metal halide perovskites that exhibit complex dynamics and are actively studied for optoelectronic applications.

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons.

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules.

Thermal Conductivity of Silicate Liquid Determined by Machine Learning Potentials

AbstractSilicate liquids are important agents of thermal evolution, yet their thermal conductivity is largely unknown. Here, we determine the thermal conductivity of a silicate liquid by combining the Green‐Kubo method with a machine learning potential of ab initio quality over the entire pressure regime of the mantle. We find that the thermal conductivity of MgSiO3 liquid is 1.1 W m−1 K−1 at the 1 bar melting point, and 4.0 W m−1 K−1 at core‐mantle boundary conditions. The thermal conductivity increases with compression, while remaining nearly constant on isochoric heating. The pressure dependence arises from the increasing bulk modulus on compression, and the weak temperature dependence arises from the saturation of the phonon mean free path due to structural disorder. The thermal conductivity of silicate liquids is less than that of ambient mantle, a contrast that may be important for understanding melt generation, and heat flux from the core.

Liquid-Liquid Critical Point in Phosphorus.

The study of liquid-liquid phase transitions has attracted considerable attention. One interesting example of this phenomenon is phosphorus, for which the existence of a first-order phase transition between a low density insulating molecular phase and a conducting polymeric phase has been experimentally established. In this Letter, we model this transition by an abinitio quality molecular dynamics simulation and explore a large portion of the liquid section of the phase diagram. We draw the liquid-liquid coexistence curve and determine that it terminates into a second-order critical point. Close to the critical point, large coupled structure and electronic structure fluctuations are observed.

A wavefunction model to chemical bonding

AbstractIn this paper we give a brief survey on some specific aspects of a wavefunction model for chemical bonding which are connected to or have been motivated in part by the work of the late István Mayer and colleagues. After a brief description of the early wavefunction models as reflected in Mayer's works, naturally leading to electron densities, we discuss localized molecular orbitals, and summarize some of the early initiatives and applications. The concept of the Chemical Hamiltonian, as introduced by Mayer, its extensions and some classical and some more advanced connections will be discussed. The twofold motivating role of Mayer in the development of the earliest, ab initio level macromolecular linear scaling methods, giving proven “ab initio quality” protein energy results by the adjustable density matrix assembler method, and in some other molecular fragment advances will be also pointed out.

Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations.

In this work, an investigation of supercritical water is presented combining inelastic and deep inelastic neutron scattering experiments and molecular dynamics simulations based on a machine-learned potential of ab initio quality. The local hydrogen dynamics is investigated at 250 bar and in the temperature range of 553-823 K, covering the evolution from subcritical liquid to supercritical gas-like water. The evolution of libration, bending, and stretching motions in the vibrational density of states is studied, analyzing the spectral features by a mode decomposition. Moreover, the hydrogen nuclear momentum distribution is measured, and its anisotropy is probed experimentally. It is shown that hydrogen bonds survive up to the higher temperatures investigated, and we discuss our results in the framework of the coupling between intramolecular modes and intermolecular librations. Results show that the local potential affecting hydrogen becomes less anisotropic within the molecular plane in the supercritical phase, and we attribute this result to the presence of more distorted hydrogen bonds.

Ab initio phase diagram and nucleation of gallium

Elemental gallium possesses several intriguing properties, such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features coexist. In order to simulate this complex system, we construct an ab initio quality interaction potential by training a neural network on a set of density functional theory calculations performed on configurations generated in multithermal–multibaric simulations. Here we show that the relative equilibrium between liquid gallium, α-Ga, β-Ga, and Ga-II is well described. The resulting phase diagram is in agreement with the experimental findings. The local structure of liquid gallium and its nucleation into α-Ga and β-Ga are studied. We find that the formation of metastable β-Ga is kinetically favored over the thermodinamically stable α-Ga. Finally, we provide insight into the experimental observations of extreme undercooling of liquid Ga.

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems

Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and the network-feature spatial derivatives which are computationally expensive. We develop a staggered NNFF architecture exploiting both rotation-invariant and covariant features separately to directly predict atomic force vectors without using spatial derivatives, thereby reducing expensive structural feature calculation by ~180-480x. This acceleration enables us to develop NNFF which directly predicts atomic forces in complex ternary and quaternary-element extended systems comprised of long polymer chains, amorphous oxide, and surface chemical reactions. The staggered rotation-invariant-covariant architecture described here can also directly predict complex covariant vector outputs from local physical structures in domains beyond computational material science.