Abstract
The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.
- Developers:
- Release Date:
- 2022-11-01
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 97294
- Site Accession Number:
- LLNL-CODE- 842508
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Zhou, Fei.
Neural Phase Simulation.
Computer Software.
https://github.com/Critical-Materials-Institute/NPS.
USDOE National Nuclear Security Administration (NNSA).
01 Nov. 2022.
Web.
doi:10.11578/dc.20221123.1.
Zhou, Fei.
(2022, November 01).
Neural Phase Simulation.
[Computer software].
https://github.com/Critical-Materials-Institute/NPS.
https://doi.org/10.11578/dc.20221123.1.
Zhou, Fei.
"Neural Phase Simulation." Computer software.
November 01, 2022.
https://github.com/Critical-Materials-Institute/NPS.
https://doi.org/10.11578/dc.20221123.1.
@misc{
doecode_97294,
title = {Neural Phase Simulation},
author = {Zhou, Fei},
abstractNote = {The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.},
doi = {10.11578/dc.20221123.1},
url = {https://doi.org/10.11578/dc.20221123.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20221123.1}},
year = {2022},
month = {nov}
}