From guesswork to guidance: How machine learning speeds dopant design for water-splitting photocatalysts

From guesswork to guidance: How machine learning speeds dopant design for water-splitting photocatalysts
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Why This Matters

Scientific discoveries like this expand human knowledge and open new possibilities for addressing global challenges.
MLIP calculations successfully identify suitable dopants for a novel photocatalytic material, report researchers from the Institute of Science Tokyo. As demonstrated in their study, published in the Journal of the American Chemical Society, a materials informatics approach could predict which ions can be stably introduced into orthorhombic Sn3O4, a promising and recently discovered photocatalytic tin oxide.
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