A monomer-centered neural network using only 1-body and 2-body permutationally invariant polynomial descriptors reproduces many-body interactions in water and CO2 at force-field-level computational cost.
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Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
A monomer-centered neural network using only 1-body and 2-body permutationally invariant polynomial descriptors reproduces many-body interactions in water and CO2 at force-field-level computational cost.