The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866907783196049408 |
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| author | Zheng, Hui Sivonxay, Eric Gallant, Max Luo, Ziyao McDermott, Matthew Huck, Patrick Persson, Kristin A. |
| author_facet | Zheng, Hui Sivonxay, Eric Gallant, Max Luo, Ziyao McDermott, Matthew Huck, Patrick Persson, Kristin A. |
| contents | Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from systematic and accurate \textit{ab initio} molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00177 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The ab initio amorphous materials database: Empowering machine learning to decode diffusivity Zheng, Hui Sivonxay, Eric Gallant, Max Luo, Ziyao McDermott, Matthew Huck, Patrick Persson, Kristin A. Materials Science Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of amorphous materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from systematic and accurate \textit{ab initio} molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. |
| title | The ab initio amorphous materials database: Empowering machine learning to decode diffusivity |
| topic | Materials Science |
| url | https://arxiv.org/abs/2402.00177 |