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Machine learned interatomic interaction potentials have enabled efficient andaccurate molecular simulations of closed systems. However, external fields,which can greatly change the chemical structure and/or reactivity, have beenseldom included in current machine learning models. This work proposes auniversal field-induced recursively embedded atom neural network (FIREANN)model, which integrates a pseudo field vector-dependent feature into atomicdescriptors to represent system-field interactions with rigorous rotationalequivariance. This "all-in-one" approach correlates various response propertieslike dipole moment and polarizability with the field-dependent potential energyin a single model, very suitable for spectroscopic and dynamics simulations inmolecular and periodic systems in the presence of electric fields. Especiallyfor periodic systems, we find that FIREANN can overcome the intrinsicmultiple-value issue of the polarization by training atomic forces only. Theseresults validate the universality and capability of the FIREANN method forefficient first-principles modeling of complicated systems in strong externalfields.

Universal Machine Learning for the Response of Atomistic Systems to External Fields