Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model

Abstract

We report a method to generate de novo protein designs through a generative adversarial neural network, MolShapeGAN, that can rapidly produce a large variety of nanoarchitected material designs inspired by proteins. The proteomic molecular designs generated by MolShapeGAN model are examined using LAMMPS coarse-grained simulations by applying tensile deformation to the longest axis of each structure, to assess mechanical properties. In order to facilitate nanomechanical optimization, we develop a transformer neural network, denoted as VoxelPerceiver, that predicts mechanical properties directly from the molecular architecture in an end-to-end fashion. The assessment of key nanomechanical properties, such as maximum tensile stress, Von Mises stress mean, and Von Mises standard deviation, offer a materiomic design paradigm by which tailored nanomechanical properties can be achieved, and by which important insights can be gained about the particularities of nanomechanical responses of molecular structures. Optimization to achieve desired mechanical properties is performed both using a brute-force grid search and Bayesian optimization. We also report manufactured samples of scaled-up architected models of the protein designs using 3D printing.

Zhenze Yang

PhD student

My research interests include computational materials science, multiscale modeling and machine learning