In recent years, machine-learned force fields (ML-FFs) have gained increasing popularity in the field of computational chemistry. Provided they are trained on appropriate reference data, ML-FFs combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electronic degrees of freedom, such as the total charge or spin , when forming their prediction. In addition, they often assume chemical locality, which can be problematic in cases where nonlocal effects play a significant role. This work introduces SpookyNet, a deep neural network for constructing ML-FFs with explicit treatment of electronic degrees of freedom and quantum nonlocality. Its predictions are further augmented with physically-motivated corrections to improve the description of long-ranged interactions and nuclear repulsion . SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it  can leverage the learned chemical insights, e.g. by predicting unknown spin states or by properly modeling physical limits. Moreover, it is able to generalize across chemical and conformational space and thus close an important remaining gap for today's machine learning models in quantum chemistry.
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs 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 ML-FFs with explicit treatment of electronic degrees of freedom and quantum nonlocality. Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits . SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets. Notably, it is able to generalize across chemical and conformational space and can leverage the learned chemical insights, e.g. by predicting unknown spin states , thus helping to close a further important remaining gap for today's machine learning models in quantum chemistry.
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In recent years, machine-learned
Machine-learned
D
have gained increasing popularity in the field of computational chemistry. Provided they are trained on appropriate reference data, ML-FFs
R
, when forming their prediction. In addition, they often
state, and
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can be problematic in cases where
is problematic when molecules have inconsistent electronic states, or when
R
Its predictions are further augmented with physically-motivated corrections to improve the description of long-ranged interactions and nuclear repulsion
Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits
A
is able to generalize across chemical and conformational space and
R
or by properly modeling physical limits. Moreover, it is able to generalize across chemical and conformational space and thus close an
, thus helping to close a further