Exploring a potential energy surface by machine learning for characterizing atomic transport

Abstract

We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for the atomic transport. The proposed method generates numerous random samples of the entire potential energy surface (PES) from a probabilistic Gaussian process model of the PES, which enables defining the likelihood of the dominant points. The robustness and efficiency of the method are demonstrated on a dozen model cases for proton diffusion in oxides, in comparison with a conventional nudge elastic band method.

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• Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
• Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
• Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning
• Prediction of perovskite-related structures in ACuO${}_{3-x}$ (A $=$ Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization
• Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
• Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
• A Sampling Strategy in Efficient Potential Energy Surface Mapping for Predicting Atomic Diffusivity in Crystals by Machine Learning
• Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal
• Robust and efficient calculation of activation energy by automated path search and density functional theory
• Multi-Hill Strategy in Metadynamics for Interstitial Diffusion in Crystals