@Article{cmc.2025.064061,
AUTHOR = {Yaping Qi, Weijie Yang},
TITLE = {From Data to Discovery: How AI-Driven Materials Databases Are Reshaping Research},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {83},
YEAR = {2025},
NUMBER = {2},
PAGES = {1555--1559},
URL = {http://www.techscience.com/cmc/v83n2/60606},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.064061}
}