Developments and further applications of ephemeral data derived potentials
Pascal T. Salzbrenner, Se Hun Joo, Lewis J. Conway, Peter I. C. Cooke, Bonan Zhu, Milosz P. Matraszek, William C. Witt, Chris J. Pickard
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Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which ...
exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.
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Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science.
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Sfedfcv/redesigned-pancake is the closest maintained adjacent implementation (Matches contextual method/domain keyword: suite). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 231 GitHub stars.
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- Sfedfcv/redesigned-pancakeAdjacentConfidence: LowStars: 231
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- jettbrains/-L-AdjacentConfidence: LowStars: 144
Matches contextual method/domain keyword: suite
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Research context
39
Citations
154
References
Tasks
Computer science, Suite, Computational science, Ternary operation, Ephemeral key, Ternary plot, Materials Science, Physical Sciences
Methods
Algorithm
Domains
Materials Chemistry
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