# SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

@article{Unke2021SpookyNetLF, title={SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects}, author={Oliver T. Unke and Stefan Chmiela and Michael Gastegger and Kristof T. Sch{\"u}tt and Huziel E. Sauceda and Klaus-Robert M{\"u}ller}, journal={Nature Communications}, year={2021}, volume={12} }

Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which is problematic when molecules have inconsistent electronic states, or when nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing machine-learned…

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