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ABSTRACT: Molecular simulations with atomistic or coarse- 6 grained force fields are a powerful approach for understanding and 7 predicting the self-assembly phase behavior of complex molecules. 8 Amphiphiles, block oligomers, and block polymers can form 9 mesophases with different ordered morphologies describing the 10 spatial distribution of the blocks, but entirely amorphous nature for 11 local packing and chain conformation. Screening block oligomer 12 chemistry and architecture through molecular simulations to find 13 promising candidates for functional materials is aided by effective 14 and straightforward morphology identification techniques. Captur- 15 ing 3-dimensional periodic structures, such as ordered network 16 morphologies, is hampered by the requirement that the number of 17 molecules in the simulated system and the shape of the periodic simulation box need to be commensurate with those of the resulting 18 network phase. Common strategies for structure identification include structure factors and order parameters, but these fail to 19 identify imperfect structures in simulations with incorrect system sizes. Building upon pioneering work by DeFever et al. [Chem. Sci. 20 2019, 10, 7503−7515] who implemented a PointNet (i.e., a neural network designed for computer vision applications using point 21 clouds) to detect local structure in simulations of single-bead particles and water molecules, we present a PointNet for detection of 22 nonlocal ordered morphologies of complex block oligomers. Our PointNet was trained using atomic coordinates from molecular 23 dynamics simulation trajectories and synthetic point clouds for ordered network morphologies that were absent from previous 24 simulations. In contrast to prior work on simple molecules, we observe that large point clouds with 1000 or more points are needed 25 for the more complex block oligomers. The trained PointNet model achieves an accuracy as high as 0.99 for globally ordered 26 morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star-block oligomers, and it also allows for the discovery 27 of emerging ordered patterns from nonequilibrium systems.more » « less
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This paper proposes a simple and fast technique for power device open circuit (OC) fault detection in stacked multicell converters (SMCs). A mitigation technique allowing for fault-tolerant operation using a simple front-end routing circuit is also proposed for SMCs. The fault detection concept only needs to sense the voltage and direction of current at the output terminal of the SMC to detect and localize an OC switch fault to a particular rail of the SMC. The proposed technique compares the measured and expected voltage levels considering the commanded switch states and the direction of the terminal current flow. Once an OC fault is detected and localized, the front-end routing circuit will be activated to reconfigure the SMC converter to a simple flying capacitor multilevel converter (FCMC) to maintain the output power flow with a reduced number of voltage levels. A window detector circuit is proposed to track the output voltage level and current direction with high bandwidth. Simulations were performed to validate the fault detection method and router performance. The functionality of windows detector is investigated with a hardware prototype 7 level 300 V SMC.more » « less