Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: An Application to Water Adsorption on Alumina

ReaxFF
is a computationally efficient model for reactive molecular
dynamics simulations that has been applied to a wide variety of chemical
systems. When ReaxFF parameters are not yet available for a chemistry
of interest, they must be (re)optimized, for which one defines a set
of training data that the new ReaxFF parameters should reproduce.
ReaxFF training sets typically contain diverse properties with different
units, some of which are more abundant (by orders of magnitude) than
others. To find the best parameters, one conventionally minimizes
a weighted sum of squared errors over all of the data in the training
set. One of the challenges in such numerical optimizations is to assign
weights so that the optimized parameters represent a good compromise
among all the requirements defined in the training set. This work
introduces a new loss function, called Balanced Loss, and a workflow
that replaces weight assignment with a more manageable procedure.
The training data are divided into categories with corresponding “tolerances”, i.e., acceptable root-mean-square errors for the categories,
which define the expectations for the optimized ReaxFF parameters.
Through the Log-Sum-Exp form of Balanced Loss, the parameter optimization
is also a validation of one’s expectations, providing meaningful
feedback that can be used to reconfigure the tolerances if needed.
The new methodology is demonstrated with a nontrivial parametrization
of ReaxFF for water adsorption on alumina. This results in a new force
field that reproduces both the rare and frequent properties of a validation
set not used for training. We also demonstrate the robustness of the
new force field with a molecular dynamics simulation of water desorption
from a γ-Al2O3 slab model.