  ReaxFF is a computationally efficient model for reactive molecular dynamics
simulations, which 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 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
between 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 is 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 non-trivial parameterization of ReaxFF for
water adsorption on alumina. This results in a new force field that reproduces
both 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 $\gamma$-Al$_2$O$_3$ slab model.
