Force Field for Water Based on Neural Network

We
developed a novel neural network-based force field for water
based on training with high-level ab initio theory. The force field
was built based on an electrostatically embedded many-body expansion
method truncated at binary interactions. The many-body expansion method
is a common strategy to partition the total Hamiltonian of large systems
into a hierarchy of few-body terms. Neural networks were trained to
represent electrostatically embedded one-body and two-body interactions,
which require as input only one and two water molecule calculations
at the level of ab initio electronic structure method CCSD/aug-cc-pVDZ
embedded in the molecular mechanics water environment, making it efficient
as a general force field construction approach. Structural and dynamic
properties of liquid water calculated with our force field show good
agreement with experimental results. We constructed two sets of neural
network based force fields: nonpolarizable and polarizable force fields.
Simulation results show that the nonpolarizable force field using
fixed TIP3P charges has already behaved well, since polarization effects
and many-body effects are implicitly included due to the electrostatic
embedding scheme. Our results demonstrate that the electrostatically
embedded many-body expansion combined with neural network provides
a promising and systematic way to build next-generation force fields
at high accuracy and low computational costs, especially for large
systems.