Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning

To
expedite new molecular compound development, a long-sought goal
within the chemistry community has been to predict molecules’
bulk properties of interest a priori to synthesis from a chemical
structure alone. In this work, we demonstrate that machine learning
methods can indeed be used to directly learn the relationship between
chemical structures and bulk crystalline properties of molecules,
even in the absence of any crystal structure information or quantum
mechanical calculations. We focus specifically on a class of organic
compounds categorized as energetic materials called high explosives
(HE) and predicting their crystalline density. An ongoing challenge
within the chemistry machine learning community is deciding how best
to featurize molecules as inputs into machine learning modelswhether
expert handcrafted features or learned molecular representations via
graph-based neural network modelsyield better results and
why. We evaluate both types of representations in combination with
a number of machine learning models to predict the crystalline densities
of HE-like molecules curated from the Cambridge Structural Database,
and we report the performance and pros and cons of our methods. Our
message passing neural network (MPNN) based models with learned molecular
representations generally perform best, outperforming current state-of-the-art
methods at predicting crystalline density and performing well even
when testing on a data set not representative of the training data.
However, these models are traditionally considered black boxes and
less easily interpretable. To address this common challenge, we also
provide a comparison analysis between our MPNN-based model and models
with fixed feature representations that provides insights as to what
features are learned by the MPNN to accurately predict density.