Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium.

ABSTRACT: We propose a novel approach for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models, specifically designed to capture phase properties across a wide range of conditions. The framework is uniquely appealing due to its ease of automation, its suitability for iterative learning, and its independence from prior knowledge of stable phases, avoiding bias towards pre-existing structural data. The approach uses Nested Sampling (NS) to explore the configuration space and generate thermodynamically relevant configurations, forming the database which undergoes ab initio Density Functional Theory (DFT) evaluation. We use the Atomic Cluster Expansion (ACE) architecture to fit a model on the resulting database. To demonstrate the efficiency of the framework, we apply

SUBMITTER: Fletcher VG 

PROVIDER: S-EPMC12815665 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature