Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes

We apply ab initio molecular dynamics (AIMD) with on-the-fly machine learning (ML) of interatomic potentials using the sparse Gaussian process regression (SGPR) algorithm for a survey of Li diffusivity in hundreds of ternary crystals as potential electrolytes for all-solid-state batteries. We show that models generated for these crystals can be easily combined for creating more general and transferable models which can potentially be used for simulating new materials without further training. As examples, universal potentials are created for Li-P-S and Li-Sb-S systems by combining the expert models of the crystals which contained the same set of elements. We also show that combinatorial models of different ternary crystals can be directly applied for modeling composite quaternary ones (e.g., Li-Ge-P-S). This hierarchical approach paves the way for modeling large-scale complexity by a combinatorial approach

First-order and continuous melting transitions in two-dimensional Lennard-Jones systems and repulsive disks

In two-dimensional Lennard-Jones (LJ) systems, a small interval of melting-mode switching occurs below which the melting occurs by first-order phase transitions in lieu of the melting scenario proposed by Kosterlitz, Thouless, Halperin, Nelson, and Young (KTHNY). The extrapolated upper bound for phase coexistence is at density rho similar to 0.893 and temperature T similar to 1.1, both in reduced LJ units. The two-stage KTHNY scenario is restored at higher temperatures, and the isothermal melting scenario is universal. The solid-hexatic and hexatic-liquid transitions in KTHNY theory, even so continuous, are distinct from typical continuous phase transitions in that instead of scale-free fluctuations, they are characterized by unbinding of topological defects, resulting in a special form of divergence of the correlation length: xi approximate to exp(b vertical bar T - T-c vertical bar(-nu)). Here such a divergence is firmly established for a two-dimensional melting phenomenon, providing a conclusive proof of the KTHNY melting. We explicitly confirm that this high-temperature melting behavior of the LJ system is consistent with the melting behavior of the r(-12) potential and that melting of the r(-n) potential is KTHNY-like for n <= 12 but melting of the r(-64) potential is first order; similar to hard disks. Therefore we suggest that the melting scenario of these repulsive potentials becomes hard-disk-like for an exponent in the range 12 < n < 64

Sparse Gaussian Process Regression-Based Machine Learned First-Principles Force-Fields for Saturated, Olefinic, and Aromatic Hydrocarbons

Universal machine learning (ML) interatomic potentials (IAPs) for saturated, olefinic, and aromatic hydrocarbons are generated by using the Sparse Gaussian process regression algorithm. The universal potentials are obtained by combining the potentials for the previously trained alkane/polyene systems and the potentials generated with the presently trained cyclic/aromatic hydrocarbon systems, along with the newly trained cross-terms between the two systems. The ML-IAPs have been trained using the PBE + D3 level of density functional theory for the on-the-fly adaptive sampling of various hydrocarbon molecules and these clusters composed of small molecules. We tested the ML-IAPs and found that they correctly predicted the structures and energies of the ??-carotene monomer and dimer. Also, the simulations of liquid ethylene reproduced the molecular volume and the simulations of toluene crystals reproduced higher stability of the ??-phase over the ??-phase. These ab initio-level force-fields could eventually evolve toward universal organic/polymeric/biomolecular systems. ?? 2022 The Authors. Published by American Chemical Society

Two Liquid-Liquid Phase Transitions in Confined Water Nanofilms

The stories behind supercooled bulk and confined water can be different. Bulk water has a metastable liquid-liquid phase transition at deeply supercooled conditions, but the existence of such a phenomenon in confined water is in question. Herein we show simulation results of first-order phase transitions between high- and low-density liquid (HDL and LDL) in confined water in both positive and negative pressures. A mid-density state between these two local states appears, which lets the transition show the hysteresis loop with transiently stable intermediate states. On the basis of Landau theory that we have adapted for mixing of moieties with high- and low-density states, we explain the phase transitions with the order parameter-dependent free energy change which is governed by second- to higher-order interactions among those moieties

Fast atomic structure optimization with on-the-fly sparse Gaussian process potentials *

We apply on-the-fly machine learning potentials (MLPs) using the sparse Gaussian process regression (SGPR) algorithm for fast optimization of atomic structures. Great acceleration is achieved even in the context of a single local optimization. Although for finding the exact local minimum, due to limited accuracy of MLPs, switching to another algorithm may be needed. For random gold clusters, the forces are reduced to similar to 0.1 eV angstrom(-1) within less than ten first-principles (FP) calculations. Because of highly transferable MLPs, this algorithm is specially suitable for global optimization methods such as random or evolutionary structure searching or basin hopping. This is demonstrated by sequential optimization of random gold clusters for which, after only a few optimizations, FP calculations were rarely needed

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

Reduced Potential Barrier of Sodium-substituted Disordered Rocksalt Cathode for Oxygen Evolution Electrocatalysts

Cation disordered rocksalt (DRX) cathodes have been viewed as next-generation high energy density materials surpassing conventional layered cathodes for lithium-ion battery (LIB) technology. Grabbing the opportunity of a better cation mixing facility in DRX, we synthesize Na-doped DRX as an efficient electrocatalyst toward oxygen evolution reaction (OER). This novel OER electrocatalyst generates a current density of 10 mA cm-2 at an overpotential (η) of 270 mV, Tafel slope of 67.5 mV dec-1, and long-term stability >5.5 days superior to benchmark IrO2 (η = 330 mV with Tafel slope = 74.8 mV dec-1). This superb electrochemical behavior is well supported by experiment and sparse Gaussian process potential (SGPP) machine learning-based search for minimum energy structure. Moreover, as oxygen binding energy (OBE) on the surface closely relates to OER activity, our density functional theory (DFT) calculations reveal that Na-doping assists in facile O2 evolution (OBE = 5.45 eV) compared with pristine-DRX (6.51 eV)

Remarkably Enhanced Lattice Oxygen Participation in Perovskites to Boost Oxygen Evolution Reaction.

Enhancing the participation of the lattice oxygen mechanism (LOM) in several perovskites to significantly boost the oxygen evolution reaction (OER) is daunting. With the rapid decline in fossil fuels, energy research is turning toward water splitting to produce usable hydrogen by significantly reducing overpotential for other half-cells' OER. Recent studies have shown that in addition to the conventional adsorbate evolution mechanism (AEM), participation of LOM can overcome their prevalent scaling relationship limitations. Here, we report the acid treatment strategy and bypass the cation/anion doping strategy to significantly enhance LOM participation. Our perovskite demonstrated a current density of 10 mA cm-2 at an overpotential of 380 mV and a low Tafel slope (65 mV dec-1) much lower than IrO2 (73 mV dec-1). We propose that the presence of nitric acid-induced defects regulates the electronic structure and thereby lowers oxygen binding energy, allowing enhanced LOM participation to boost OER significantly