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.

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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

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article

Materials by machines: A review on intuition and knowledge-driven learning for rapid discovery and optimization

Prasad Rangaraju, M.Z. Naser
Materials Research Bulletin
Machine Learning in Materials Science
article

Materials by machines: A review on intuition and knowledge-driven learning for rapid discovery and optimization

Prasad Rangaraju, M.Z. Naser
article en

Abstract

• 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.

Materials Research BulletinVol. 204
Artificial Intelligence in Medicine (Canada) (CA), Clemson University (US)
Portland Cement Association
Openalex Percentile: Top 12%
Machine Learning in Materials Science
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