From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

• DOI: 10.48550/arxiv.2403.05724
• Publication Date: 2024-03-08
• Authors: Sarath Menon; Yury Lysogorskiy; A. Knoll; Niklas Leimeroth; Marvin Poul; Minaam Qamar; Jan Janßen; Matous Mrovec; Jochen Rohrer; Karsten Albe; Jörg Behler; Ralf Drautz; Jörg Neugebauer
• Keywords: Condensed Matter - Materials Science; Materials Science (cond-mat.mtrl-sci); FOS: Physical sciences

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