Machine Learning Potential
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Machine learning potentials (MLP) are a class of models trained on first-principles data to approximate potential energy surfaces, with typical representatives including MACE, CHGNet, M3GNet, and the ORB series.
The accuracy of MLPs is limited by the training distribution, and large errors may occur for structures containing rare elements or extreme coordination, so their main role is to provide consistent and efficient relative ranking rather than to replace high-accuracy validation.
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Machine learning potentials (MLP) are a class of models trained on first-principles data to approximate potential energy surfaces, with typical representatives including MACE, CHGNet, M3GNet, and the ORB series.
50%UnverifiedThe accuracy of MLPs is limited by the training distribution, and large errors may occur for structures containing rare elements or extreme coordination, so their main role is to provide consistent and efficient relative ranking rather than to replace high-accuracy validation.
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