Machine Learning the Metastable Phase Diagram of Materials
| Autor: | Henry Chan, Troy D. Loeffler, Sukriti Manna, Jianguo Wen, Duan Luo, Liuxiang Yang, Wenge Yang, Subramanian K. R. S. Sankaranarayanan, Srilok Srinivasan, Pierre Darancet, Rohit Batra |
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| Rok vydání: | 2020 |
| Předmět: |
Metastable phase diagram
Elemental composition Condensed Matter - Materials Science Materials science Condensed Matter - Mesoscale and Nanoscale Physics Statistical Mechanics (cond-mat.stat-mech) Thermodynamic equilibrium business.industry Materials Science (cond-mat.mtrl-sci) FOS: Physical sciences Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks Machine learning computer.software_genre Metastability Phase (matter) Mesoscale and Nanoscale Physics (cond-mat.mes-hall) Artificial intelligence business computer Energy (signal processing) Condensed Matter - Statistical Mechanics Material synthesis Phase diagram |
| DOI: | 10.48550/arxiv.2004.08753 |
| Popis: | Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial estimate of thermodynamically accessible phases, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to inaccessible regions. Such an approach, combined with first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases albeit at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local free energy minimum, that may exhibit desirable properties. Mapping these metastable phases and their thermodynamic behavior is highly desirable but currently lacking. Here, we introduce an automated workflow that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition. Using a representative material, carbon, with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic mapping of hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium. Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for construction of metastable phase diagrams. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy are used to validate our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems. Comment: 32 pages 16 figures; v3: Added acknowledgments and removed affiliations from author metadata |
| Databáze: | OpenAIRE |
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