arXiv (All)
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Multitask learning with semiempirical orbital charges enables sample-efficient MLIPs
Machine learning interatomic potentials (MLIPs) require generating computationally expensive, large-scale training datasets to accurately simulate materials and molecules. Incorporating electronic structure information using multitask learning improves sample efficiency, however, training on full Ha
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