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Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
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作者 Vincent G.Fletcher Albert P.Bartók Livia B.Pártay 《npj Computational Materials》 2025年第1期4488-4506,共19页
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 i... 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.Weuse the Atomic Cluster Expansion(ACE)architecture to fit a model on the resulting database.To demonstrate the efficiency of the framework,we apply it to magnesium,developing a model capable of accurately describing behaviour across pressure and temperature ranges of 0–600 GPa and 0–8000 K,respectively.We benchmark themodel’s performance by calculating phonon spectra and elastic constants,as well as the pressuretemperature phase diagram within this region.The results showcase the power of the framework to produce robust MLIPs while maintaining transferability and generality,for reduced computational cost.UK Ministry of Defence©Crown Owned Copyright 2025/AWE. 展开更多
关键词 atomic cluster expansion machine learned interatomic potentials MAGNESIUM thermodynamically informed nested sampling AUTONOMOUS database generation iterative learningand
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