jarvis_leaderboard
Project description
JARVIS-Leaderboard:
This project provides benchmark-performances of various methods for materials science applications using the datasets available in JARVIS-Tools databases. Some of the methods are: Artificial Intelligence (AI), Electronic Structure (ES), Force-field (FF), Qunatum Computation (QC) and Experiments (EXP). There are a variety of properties included in the benchmark. In addition to prediction results, we attempt to capture the underlyig software, hardware and instrumental frameworks to enhance reproducibility. This project is a part of the NIST-JARVIS infrastructure.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for jarvis_leaderboard-2023.6.12.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | bfa1f030b95309fb2f374be91d3bb3632fadc5643399d5020332689778ad04ec |
|
MD5 | 617c8c93a631f4a5a07ce0fed38a1e3e |
|
BLAKE2b-256 | eaa3d92e005e6fc16fae9c7b209999a5f52b87abb5eb74867f7bd89d51bcb22b |
Close
Hashes for jarvis_leaderboard-2023.6.12-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 093a27c3e0cbc8e837a055a615867738e613c3952428fce0744ea9594711b677 |
|
MD5 | b5e452c250832454d36ba5f3035e58b5 |
|
BLAKE2b-256 | 2cf92aee7fe5044a3ed5642bedffa13007a5d302a6c93dc27c2ed4c86c3ee041 |