Modeling Zinc Complexes Using Neural Networks

Understanding the energetic landscapes of large molecules
is necessary
for the study of chemical and biological systems. Recently, deep learning
has greatly accelerated the development of models based on quantum
chemistry, making it possible to build potential energy surfaces and
explore chemical space. However, most of this work has focused on
organic molecules due to the simplicity of their electronic structures
as well as the availability of data sets. In this work, we build a
deep learning architecture to model the energetics of zinc organometallic
complexes. To achieve this, we have compiled a configurationally and
conformationally diverse data set of zinc complexes using metadynamics
to overcome the limitations of traditional sampling methods. In terms
of the neural network potentials, our results indicate that for zinc
complexes, partial charges play an important role in modeling the
long-range interactions with a neural network. Our developed model
outperforms semiempirical methods in predicting the relative energy
of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol
with reference to the double-hybrid PWPB95 method.