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

ZZ Yang and YC Hsu and MJ Buehler, JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS, 170, 105098 (2023).

DOI: 10.1016/j.jmps.2022.105098

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 nano -mechanical 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. Opti-mization 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.

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