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

作　　者：Yaping Qi Weijie Yang 

机构地区：[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

出　　处：《Computers, Materials & Continua》2025年第5期1555-1559,共5页计算机、材料和连续体(英文)

摘　　要：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.

关 键 词：DATA-DRIVEN materials database AI assistant materials design 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]