Space Group Informed Transformer for Crystalline Materials Generation
We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. The incorporation of space group symmetry significantly simplifies the crystal space, which is crucial for data and compute efficient generative...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
23.03.2024
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Subjects | |
Online Access | Get full text |
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Summary: | We introduce CrystalFormer, a transformer-based autoregressive model
specifically designed for space group-controlled generation of crystalline
materials. The incorporation of space group symmetry significantly simplifies
the crystal space, which is crucial for data and compute efficient generative
modeling of crystalline materials. Leveraging the prominent discrete and
sequential nature of the Wyckoff positions, CrystalFormer learns to generate
crystals by directly predicting the species and locations of
symmetry-inequivalent atoms in the unit cell. We demonstrate the advantages of
CrystalFormer in standard tasks such as symmetric structure initialization and
element substitution compared to conventional methods implemented in popular
crystal structure prediction software. Moreover, we showcase the application of
CrystalFormer of property-guided materials design in a plug-and-play manner.
Our analysis shows that CrystalFormer ingests sensible solid-state chemistry
knowledge and heuristics by compressing the material dataset, thus enabling
systematic exploration of crystalline materials. The simplicity, generality,
and flexibility of CrystalFormer position it as a promising architecture to be
the foundational model of the entire crystalline materials space, heralding a
new era in materials modeling and discovery. |
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DOI: | 10.48550/arxiv.2403.15734 |