Machine learning the metastable phase diagram of covalently bonded carbon
Exploration of metastable phases of a given elemental composition is a data-intensive task. Here the authors integrate first-principles atomistic simulations with machine learning and high-performance computing to allow a rapid exploration of the metastable phases of carbon.
Bibliographic Details
| Main Authors: |
Srilok Srinivasan,
Rohit Batra,
Duan Luo,
Troy Loeffler,
Sukriti Manna,
Henry Chan,
Liuxiang Yang,
Wenge Yang,
Jianguo Wen,
Pierre Darancet,
Subramanian K.R.S. Sankaranarayanan |
|---|
| Format: | Article
|
|---|
| Language: | English |
|---|
| Published: |
Nature Portfolio
2022-06-01
|
|---|
| Series: | Nature Communications
|
|---|
| Online Access: | https://doi.org/10.1038/s41467-022-30820-8
|
|---|