Integration of materials science and artificial intelligence: From high‐throughput screening to autonomous laboratories

作者：Pengfei Huang, Wei‐Di Liu, Chenhua Sun, Zekun Li, Yu Wang, Yanan Chen · 发表于：Materials Genome Engineering Advances · 年份：2025 · DOI：10.1002/mgea.70036 · 被引用次数：9 · 研究领域：Machine Learning in Materials Science、Catalysis and Oxidation Reactions、Electrocatalysts for Energy Conversion

Abstract Traditional methods for material discovery and optimization are time‐consuming and resource‐consuming. Recent advancements in artificial intelligence (AI), particularly machine learning, offer a revolutionary opportunity for accelerating novel material discovery. This review overviews AI enhancement on high‐throughput synthesis and screening methods for faster and more efficient material discovery, focusing on electrocatalysis and energy storage materials. The integration of AI with autonomous laboratories allows real‐time data analysis and closed‐loop optimization, accelerating material characterization and analysis. Despite challenges in data quality and model transparency, integration of AI with experimental workflows significantly advances materials science.