(474j) Training Accurate and Physically Meaningful Machine Learning Force Fields for Water and Understanding Their Transferability

Water is an important material relevant to biology, chemistry, and catalysis among others, however, the accurate simulation of water is challenging because it requires long molecular dynamics simulations to equilibrate the system due to the complex interactions between the molecules. The MB-Pol potential has been developed to reproduce CCSD(T) quality results, but at a lower cost than full quantum mechanical simulations and allows to reproduce the ground truth for physical properties (e.g. density, compressibility, radial distribution function, etc.) which are typically inaccessible using quantum mechanical calculations due to long equilibration times (10 – 100 ns). Machine learning force fields (MLFFs) serve as an alternative to accelerate atomistic simulations by learning the complex set of interactions from existing data. We show that the Allegro extension of NequIP can accurately learn from liquid, gas, and solid phases of H₂O. The transferability of MLFF models trained on different phases is also explored to determine what information is crucial to the resulting MLFF. Importantly, we find that our MLFFs can describe physically meaningful many-body interactions that are not directly trained on and allow even faster simulations than MB-pol. In addition, MLFFs will be possible to combine with additional data to describe more complex systems than water only that are directly relevant to practical problems in biology, chemistry, and catalysis.