This repository hosts the public website for the CMS Center for Machine Learning Accelerated Materials Discovery (MLAMD):
The site presents the center's scientific vision, current research directions, news, publications, software, data products, team members, and future roadmap. It complements the Ames National Laboratory center page by providing a project-maintained technical microsite that can be updated as exa-AMD, exa-PD, datasets, and publications evolve.
The website is designed for several audiences:
- Researchers who want to understand the scientific ideas behind AI/ML-assisted materials discovery and exascale phase-diagram prediction.
- DOE program managers and reviewers who need a clear view of project progress, deliverables, publications, and future directions.
- Collaborators and users who want access to released software, documentation, datasets, and citable records.
- Project members who need a central public-facing record of people, research, news, publications, software, data, and roadmap.
index.html- landing page and project overview.research.html- Thrust 1 exa-AMD and Thrust 2 exa-PD research workflows, modules, progress, and code links.news.html- recent research directions, meetings, publications, software releases, datasets, and project news.publications.html- papers, preprints, manuscripts, and workflow outputs.data.html- software, datasets, databases, OSTI records, Zenodo records, GitHub repositories, and documentation links.people.html- leadership, key personnel, research team members, oversight committee, and advisory committee.roadmap.html- near-term plans and DOE-relevant future impact.
- Ames Lab center page: https://www.ameslab.gov/machine-learning-accelerated-materials-discovery-center
- ISU ML materials page: https://ml-material.physics.iastate.edu/
- exa-AMD documentation: https://ml-amd.github.io/exa-amd/
- exa-PD documentation: https://ml-amd.github.io/exa-pd/
- exa-AMD code: https://github.com/ML-AMD/exa-amd
- exa-PD code: https://github.com/ML-AMD/exa-pd
Maintained by Weiyi Xia.