TY  - EJOU
AU  - Qi, Yaping 
AU  - Yang, Weijie 

TI  - From Data to Discovery: How AI-Driven Materials Databases Are Reshaping Research
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Data-driven; materials database; AI assistant; materials design

DO  - 10.32604/cmc.2025.064061