From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911792427433984 |
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| author | Menon, Sarath Lysogorskiy, Yury Knoll, Alexander L. M. Leimeroth, Niklas Poul, Marvin Qamar, Minaam Janssen, Jan Mrovec, Matous Rohrer, Jochen Albe, Karsten Behler, Jörg Drautz, Ralf Neugebauer, Jörg |
| author_facet | Menon, Sarath Lysogorskiy, Yury Knoll, Alexander L. M. Leimeroth, Niklas Poul, Marvin Qamar, Minaam Janssen, Jan Mrovec, Matous Rohrer, Jochen Albe, Karsten Behler, Jörg Drautz, Ralf Neugebauer, Jörg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron Menon, Sarath Lysogorskiy, Yury Knoll, Alexander L. M. Leimeroth, Niklas Poul, Marvin Qamar, Minaam Janssen, Jan Mrovec, Matous Rohrer, Jochen Albe, Karsten Behler, Jörg Drautz, Ralf Neugebauer, Jörg Materials Science 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. |
| title | From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron |
| topic | Materials Science |
| url | https://arxiv.org/abs/2403.05724 |