From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要