Artificial intelligence is increasingly being used to discover new materials, but conventional data-driven approaches can struggle to explain their predictions, work reliably beyond their training data and remain consistent with physical laws. A new perspective proposes a framework called Physics-Grounded Materials AI (PhysMat AI), which integrates fundamental physical knowledge into the materials discovery process. The research is published in the journal Advanced Functional Materials.
"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."
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