Screening block oligomer chemistry and architecture through molecular simulations to find promising candidates for functional materials requires effective morphology identification techniques. Common strategies for structure identification include structure factors and order parameters, but these fail to identify imperfect structures in simulations with incorrect system sizes.
We developed a machine learning approach, called PointNet-meso, for mesophase classification including different network morphologies (double gyroid, single gyroid, double diamond, and plumber’s nightmare). The trained PointNet-meso model achieves an accuracy as high as 0.99 for globally ordered morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star block oligomers, and it also allows for the discovery of emerging ordered patterns from non-equilibrium systems.
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UMN Materials Research Science and Engineering Center
This multifaceted MRSEC enables important areas of future technology, ranging from applications of electrical control over materials to scale-invariant shape-filling amphiphile network self-assembly. The UMN MRSEC manages an extensive program in education and career development. The MRSEC is bolstered by a broad complement of over 20 companies that contribute directly to IRG research through intellectual, technological, and financial support. International research collaborations and student exchanges are pursued with leading research labs in Asia and Europe.