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Geometry-enhanced pretraining on interatomic potentials - Nature Machine Intelligence

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Geometry-enhanced pretraining on interatomic potentials - Nature Machine Intelligence

Machine learning interatomic potentials (MLIPs) describe the interactions between atoms in materials and molecules by learning them from a reference database generated by ab initio calculations. MLIPs can accurately and efficiently predict such interactions and have been applied to various fields of physical science. However, high-performance MLIPs rely on a large amount of labelled data, which are costly to obtain by ab initio calculations.

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Geometry-enhanced pretraining on interatomic potentials - Nature Machine Intelligence

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Geometry-enhanced pretraining on interatomic potentials - Nature Machine Intelligence

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