From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

S Menon and Y Lysogorskiy and ALM Knoll and N Leimeroth and M Poul and M Qamar and J Janssen and M Mrovec and J Rohrer and K Albe and J Behler and R Drautz and J Neugebauer, NPJ COMPUTATIONAL MATERIALS, 10, 261 (2024).

DOI: 10.1038/s41524-024-01441-0

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high- dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

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