Self-supervised learning for crystal property prediction via denoising
<|reference_start|>Self-supervised learning for crystal property prediction via denoising: Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of interest, the number of materials for which a specific property has been determined is much smaller than the number of known materials. To overcome this disparity, we propose a novel self-supervised learning (SSL) strategy for material property prediction. Our approach, crystal denoising self-supervised learning (CDSSL), pretrains predictive models (e.g., graph networks) with a pretext task based on recovering valid material structures when given perturbed versions of these structures. We demonstrate that CDSSL models out-perform models trained without SSL, across material types, properties, and dataset sizes.<|reference_end|>
@article{new2024self-supervised,
    title={Self-supervised learning for crystal property prediction via denoising},
    author={Alexander New, Nam Q. Le, Michael J. Pekala, Christopher D. Stiles},
    journal={arXiv preprint arXiv:2408.17255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17255},
    primaryClass={cs.LG cond-mat.mtrl-sci}
}