Abstract
ML-AMD is a Python workflow framework designed to accelerate the discovery and design of functional materials.
- Developers:
-
Moraru, Maxim [1] ; Li, Ying Wai [1] ; Xia, Weiyi [2] ; Zhang, Feng [2]
- Los Alamos National Laboratory
- Ames Laboratory (AMES), Ames, IA (United States)
- Release Date:
- 2025-05-30
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Office of Science (SC), Basic Energy Sciences (BES)Primary Award/Contract Number:AC02-07CH11358USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:89233218CNA000001
- Code ID:
- 156263
- Research Org.:
- Ames Laboratory (AMES), Ames, IA (United States)Idaho National Laboratory (INL), Idaho Falls, ID (United States)
- Country of Origin:
- United States
Citation Formats
Moraru, Maxim, Li, Ying Wai, Xia, Weiyi, and Zhang, Feng.
ML-AMD/exa-amd.
Computer Software.
https://github.com/ML-AMD/exa-amd.
USDOE Office of Science (SC), Basic Energy Sciences (BES), USDOE National Nuclear Security Administration (NNSA).
30 May. 2025.
Web.
doi:10.11578/dc.20250530.10.
Moraru, Maxim, Li, Ying Wai, Xia, Weiyi, & Zhang, Feng.
(2025, May 30).
ML-AMD/exa-amd.
[Computer software].
https://github.com/ML-AMD/exa-amd.
https://doi.org/10.11578/dc.20250530.10.
Moraru, Maxim, Li, Ying Wai, Xia, Weiyi, and Zhang, Feng.
"ML-AMD/exa-amd." Computer software.
May 30, 2025.
https://github.com/ML-AMD/exa-amd.
https://doi.org/10.11578/dc.20250530.10.
@misc{
doecode_156263,
title = {ML-AMD/exa-amd},
author = {Moraru, Maxim and Li, Ying Wai and Xia, Weiyi and Zhang, Feng},
abstractNote = {ML-AMD is a Python workflow framework designed to accelerate the discovery and design of functional materials.},
doi = {10.11578/dc.20250530.10},
url = {https://doi.org/10.11578/dc.20250530.10},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250530.10}},
year = {2025},
month = {may}
}