University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining using Wyckoff Representations

Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining using Wyckoff Representations

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Coarse-graining to molecular graphs is critical to ML-driven molecular discovery. We show how Wyckoff representations – coordinate-free sets of symmetry-related positions in crystals – can empower analogous improvements in inorganic material discovery. Unlike atomic positions, Wyckoff representations are computably enumerable enabling screening campaigns across novel materials space. Critically, compared to composition-based approaches we avoid the majority of the cost of structure searching when validating predictions. We propose “Wren” – a ML model operating on Wyckoff representations. We demonstrate Wren’s potential by identifying 1,558 novel materials that lie below the convex hull of previously calculated materials from just 5,675 ab-initio calculations. arXiv pre-print: https://arxiv.org/abs/2106.11132

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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