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- W3167821000 abstract "Traditional, data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates that excel in a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach shifts the optimization focus from model parameters to the fractions of each element in a composition. Using a pretrained network, CrabNet, and writing a custom loss function to govern a vector of element fractions, compositions can be optimized such that a predicted property is maximized or minimized. Single and multi-property optimization examples are presented that highlight the capabilities and robustness of this approach to inverse design." @default.
- W3167821000 created "2021-06-22" @default.
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- W3167821000 date "2021-05-28" @default.
- W3167821000 modified "2023-09-24" @default.
- W3167821000 title "Optimizing Fractional Compositions To Achieve Extraordinary Properties" @default.
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- W3167821000 doi "https://doi.org/10.26434/chemrxiv.14680578.v1" @default.
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