CrysMTM: a multiphase, temperature-resolved, multimodal dataset for crystalline materials
We present CrysMTM, a large-scale, multimodal dataset designed to benchmark temperature- and phase-sensitive machine learning models for crystalline materials. The dataset comprises approximately 30 000 atomistic samples covering the three primary polymorphs of titanium dioxide–anatase, brookite, an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adf9bc |
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