Open Access

COMMENTARY

From Data to Discovery: How AI-Driven Materials Databases Are Reshaping Research

Yaping Qi^1,*, Weijie Yang^2,*

1 Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan
2 Department of Power Engineering, North China Electric Power University, Baoding, 071003, China

* Corresponding Authors: Yaping Qi. Email: ; Weijie Yang. Email:

Computers, Materials & Continua 2025, 83(2), 1555-1559. https://doi.org/10.32604/cmc.2025.064061

Received 03 February 2025; Accepted 18 March 2025; Issue published 16 April 2025

Abstract

AI-driven materials databases are transforming research by integrating experimental and computational data to enhance discovery and optimization. Platforms such as Digital Catalysis Platform (DigCat) and Dynamic Database of Solid-State Electrolyte (DDSE) demonstrate how machine learning and predictive modeling can improve catalyst and solid-state electrolyte development. These databases facilitate data standardization, high-throughput screening, and cross-disciplinary collaboration, addressing key challenges in materials informatics. As AI techniques advance, materials databases are expected to play an increasingly vital role in accelerating research and innovation.

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Keywords

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