Automated discovery of a robust interatomic potential for aluminum

Abstract

Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM) calculations, but these become prohibitively expensive at large simulation scales. Machine learning (ML) based potentials aim for faithful emulation of QM at drastically reduced computational cost. The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset. Using the principles of active learning (AL), we present a highly automated approach to dataset construction. The strategy is to use the ML potential under development to sample new atomic configurations and, whenever a configuration is reached for which the ML uncertainty is sufficiently large, collect new QM data. Here, we seek to push the limits of automation, removing as much expert knowledge from the AL process as possible. All sampling is performed using MD simulations starting from an initially disordered configuration, and undergoing non-equilibrium dynamics as driven by time-varying applied temperatures. We demonstrate this approach by building an ML potential for aluminum (ANI-Al). After many AL iterations, ANI-Al teaches itself to predict properties like the radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics. Interestingly, the configurations appearing in shock appear to have been well sampled in the AL training dataset, in a way that we illustrate visually.

References in corpus (7)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Machine-learning based interatomic potential for amorphous carbon
• Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
• Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
• Bulk Aluminum at High Pressure: A First-Principles Study

Cited by in corpus (13)

• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium
• Deep Coarse-grained Potentials via Relative Entropy Minimization
• Modeling nanoconfinement effects using active learning
• Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
• Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations
• Data efficiency and extrapolation trends in neural network interatomic potentials
• A "Magnetic" Machine Learning Interatomic Potential for Nickel
• Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles
• Supervised and Unsupervised Machine Learning of Structural Phases of Polymers Adsorbed to Nanowires
• Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys
• Nanoindentation simulations for copper and tungsten with adaptive-precision potentials
• Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure