An automated framework for exploring and learning potential-energy surfaces

2024/12/21 by Yuanbin Liu, Liu, Yuanbin, Joe D. Morrow +16 · 1 voice · 8 citations
Materials Science · #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2412.16736

Abstract

Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex (`automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science.

Citations

• LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
• Machine Learning for Molecular Simulation
• Less is more: Sampling chemical space with active learning

Cited by

• NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials
• Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems
• A Software Package for Generating Robust and Accurate Potentials using the Moment Tensor Potential Framework
• Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
• Autonomous interpretation of atomistic scattering data
• A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
• PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential
• Medium-range structural order in amorphous arsenic

Discussions

• Meet autoplex – our approach to automated ML potential fitting, built jointly with @molecularxtal.bsky.social & team in Berlin! In this preprint, we focus on exploring structures and training potentia [bsky, 36 points, 1 comments]

Related

• Fine-Tuning Bulk-oriented Universal Interatomic Potentials for Surfaces: Accuracy, Efficiency, and Forgetting Control
• Beyond potentials: integrated machine-learning models for materials
• A Foundational Potential Energy Surface Dataset for Materials
• Optimal data generation for machine learned interatomic potentials
• Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency