Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2025.064061
Journal Menu
Special Issues
Table of Content

All issues

Online First

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

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

Yaping Qi1,*, Weijie Yang2,*
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 Author: Yaping Qi. Email: email; Weijie Yang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.064061

Received 03 February 2025; Accepted 18 March 2025; Published online 26 March 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.

Keywords

Data-driven; materials database; AI assistant; materials design

  • 84

    View

  • 16

    Download

  • 0

    Like

Related articles

Share Link