U.S. Department of EnergyOffice of Scientific and Technical Information
The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
Abstract
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 ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate 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 conductiv- ity. 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 uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.
- Authors:
-
;
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); LBNL
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Publication Date:
- DOE Contract Number:
- AC02-05CH11231
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Collaborations:
- Materials Project
- Subject:
- 36 MATERIALS SCIENCE
- OSTI Identifier:
- 2281590
- DOI:
- https://doi.org/10.17188/mpcontribs/2281590
Citation Formats
Zheng, Hui, and Huck, Patrick. The ab initio amorphous materials database: Empowering machine learning to decode diffusivity. United States: N. p., 2024.
Web. doi:10.17188/mpcontribs/2281590.
Zheng, Hui, & Huck, Patrick. The ab initio amorphous materials database: Empowering machine learning to decode diffusivity. United States. doi:https://doi.org/10.17188/mpcontribs/2281590
Zheng, Hui, and Huck, Patrick. 2024.
"The ab initio amorphous materials database: Empowering machine learning to decode diffusivity". United States. doi:https://doi.org/10.17188/mpcontribs/2281590. https://www.osti.gov/servlets/purl/2281590. Pub date:Tue Jan 16 23:00:00 EST 2024
@article{osti_2281590,
title = {The ab initio amorphous materials database: Empowering machine learning to decode diffusivity},
author = {Zheng, Hui and Huck, Patrick},
abstractNote = {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 ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate 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 conductiv- ity. 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 uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.},
doi = {10.17188/mpcontribs/2281590},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 16 23:00:00 EST 2024},
month = {Tue Jan 16 23:00:00 EST 2024}
}