Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Abstract The discovery of multicomponent inorganic compounds can provide direct solutions to scientific and engineering challenges, yet the vast uncharted material space dwarfs synthesis throughput. While the crystal structure prediction (CSP) may mitigate this frustration, the exponential complexit...
Main Authors: | Sungwoo Kang, Wonseok Jeong, Changho Hong, Seungwoo Hwang, Youngchae Yoon, Seungwu Han |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2022-05-01
|
Series: | npj Computational Materials |
Online Access: | https://doi.org/10.1038/s41524-022-00792-w |
Similar Items
-
Production of thin film of multicomponent inorganic semiconductors under quasi-equilibrium conditions
by: B. Tsizh, et al.
Published: (2022-06-01) -
Prediction of multicomponent inorganic atmospheric aerosol behavir/
by: 176679 Ansari, Asif S., et al. -
Applications and training sets of machine learning potentials
by: Changho Hong, et al.
Published: (2023-10-01) -
Out-of-equilibrium processes in crystallization of organic-inorganic perovskites during spin coating
by: Shambhavi Pratap, et al.
Published: (2021-09-01) -
Multicomponent Reactions Accelerated by Aqueous Micelles
by: Daniel Paprocki, et al.
Published: (2018-10-01)