Generative Deep Learning-Aided Design of Flexible Molecular Crystals

Organic molecular crystals capable of mechanical adaptation
are
poised to revolutionize soft advanced materials with potentially immense
implications from optics to electronics and biomedicine. While these
prospects have guided studies into both fundamental and performance
aspects, currently available methodologies for the design of organic
crystalline matter with specific mechanical properties based on classical
crystal engineering principles lack reliability and generality in
application, and this significantly limits consideration of organic
crystals as materials of choice. To address this challenge, we apply
deep learning models for the design of mechanically compliant organic
crystalline materials. We introduce CrystalGAN, a deep generative
framework based on a generative adversarial network (GAN), designed
to efficiently generate flexible molecular crystals with desired mechanical
properties. CrystalGAN leverages a graph convolutional network (GCN)
to construct both the generator and discriminator of a Wasserstein
GAN (WGAN), enhancing the validity of the generated molecules. A convolutional
neural network (CNN) was trained to predict and discriminate the mechanical
properties of unknown molecules, based on the data collected from
the extant literature and compared with multilayer perceptron (MLP)
with backpropagation algorithm. The CNN showed favorable performance
with high accuracy in various computational evaluations and successfully
predicted the mechanical response of the flexible crystals. The inferences
are complemented by case studies that employed CrystalGAN and CNN
to generate molecules that are expected to crystallize as flexible
crystals with improved tableting properties. This work overcomes one
of the current major challenges with the lack of discovery and prediction
of organic crystals with specific mechanical properties.