Organization · North America
Adaptis
Materials, Waste & Circular Economy
Scientific Paper · source-verified
A Framework to Evaluate Machine Learning Crystal Stability Predictions
Riebesell, Janosh; Goodall, Rhys E. A.; Benner, Philipp; Chiang, Yuan; Deng, Bowen; Ceder, Gerbrand; Asta, Mark; Lee, Alpha A.; Jain, Anubhav; Persson, Kristin A. · 2025-05-09
Matbench Discovery is a benchmarking framework for machine learning models used in materials discovery, highlighting the importance of task-specific metrics and demonstrating that universal interatomic potentials can effectively pre-screen stable inorganic materials in high-throughput workflows.
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Riebesell, Janosh; Goodall, Rhys E. A.; Benner, Philipp; Chiang, Yuan; Deng, Bowen; Ceder, Gerbrand; Asta, Mark; Lee, Alpha A.; Jain, Anubhav; Persson, Kristin A.. 2025-05-09. A Framework to Evaluate Machine Learning Crystal Stability Predictions. https://www.nature.com/articles/s42256-025-01055-1.epdf?sharing_token=N5dTEbyOD6QKy6FqnDBy0dRgN0jAjWel9jnR3ZoTv0PaSYjLgi7y4a9LlN5TLXDR9GV8xb903gJNOmMcvEA1s-IflC0MqzHFlYb05uaO2ZVcxc-CBkK1a1bKsjpAEOJUM6we7W9XYousvJRsLsk_y_9a-ZvEp4bgGlLEXvRvw-Q%3D (AI & Environment Resource Hub; record paper-325; collection snapshot 2026-09-15).
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Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
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Available source metadata
- Author or creator
- Riebesell, Janosh; Goodall, Rhys E. A.; Benner, Philipp; Chiang, Yuan; Deng, Bowen; Ceder, Gerbrand; Asta, Mark; Lee, Alpha A.; Jain, Anubhav; Persson, Kristin A.
- Publisher
- Nature
- Publication date
- 2025-05-09
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:58:06Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Materials, Waste & Circular Economy
- Legacy subdomain
- Machine Learning Benchmarking for Thermodynamic Stability in Materials Discovery
- journal
- Nature
Provenance and review
Source sheet: Scientific Paper · Row 329 · Original ID: paper-325.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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Shared topics: Materials and critical minerals; E-waste and circularity
Shared topics: Materials and critical minerals; E-waste and circularity
Organization · North America
AIMATX
Materials, Waste & Circular Economy
Shared topics: Materials and critical minerals; E-waste and circularity
Multimedia · 2025-03-14
America Wastes $6+ Billion Worth Of Recyclables A Year. Can Robots And AI Help? | AI In Action
The United States throws away $6.5 billion worth of reusable material every year, and the recycling rate has remained flat for over a decade. Now, tech companies are using artificial intelligence and robotics to make the process safer, more efficient, and more common. But can they really raise the recycling rate in a country with more than 9,000 recycling programs?
Shared topics: Materials and critical minerals; E-waste and circularity
Organization · North America
Anthrogen
Materials, Waste & Circular Economy