Materials by machines: A review on intuition and knowledge-driven learning for rapid discovery and optimization
• Four AI frontiers integrated into one closed-loop materials discovery cycle. • Foundation models and LLMs propose viable compounds and synthesis routes. • Explainable AI interprets predictions and surfaces candidate design rules. • Causal inference moves beyond correlation to identify property mechanisms. • Autonomous laboratories run, analyze, and learn from experiments continuously.
Authors
- Prasad Rangaraju (ORCID: https://orcid.org/0000-0003-4285-1252)
- M.Z. Naser (ORCID: https://orcid.org/0000-0003-1350-3654)
Institutions
- Artificial Intelligence in Medicine (Canada) (CA)
- Clemson University (US)
Publication Details
- Journal
- Materials Research Bulletin
- Published
- 2026-05-29
- DOI
- https://doi.org/10.1016/j.materresbull.2026.114251
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Portland Cement Association