Nanowerk
Physics-grounded AI framework targets more reliable materials discovery
Overall closed-loop workflow of Physics-Grounded Materials AI (PhysMat AI). It describes a closed-loop materials-discovery workflow that integrates physical principles, curated databases, AI models and agents, prediction and screening, experimental validation, and continuous…
"Materials discovery cannot rely on correlations in data alone," says Hao Li, Distinguished Professor at the Advanced Institute for Materials Research (WPI-AIMR) at Tohoku University. "By incorporating physical principles into AI, we can make its predictions more interpretable, testable and meaningful from a materials science perspective."
The behavior of materials is governed by factors including thermodynamics, kinetics, electronic structure, transport processes and operating environments.
The researchers argue that incorporating this knowledge into AI systems can help move materials…
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