An automated framework for exploring and learning potential-energy surfaces.

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 titaniu

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12361387 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature