Bridging AI- and experimental-led materials discovery with better database architecture
- Apr 12
- 1 min read
PHYS.org— Materials databases lie at the heart of future data-driven discovery in energy-related fields, say researchers from Tohoku University. In an article published in the journal Precision Chemistry, they have examined how different types of databases, both computational and experimental, work together to support modern artificial intelligence (AI) tools used in materials science.
The study found that materials databases are no longer just places to store information. Instead, they play a central role in determining how well AI models perform.
