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

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.

Keywords

artificial intelligence / autonomous laboratories / closed-loop optimization / electrocatalysis / energy storage materials / high-throughput experimentation

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.