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Multi-fidelity graph networks learn more effective representations for materials from large data sets of low-fidelity properties, which can then be used to make accurate predictions of high-fidelity properties, such as the band gaps of ordered and disordered crystals and energies of molecules.
Computational simulations show that selection for high gene expression stability can explain the stable maintenance of obsolete phenotypic switching capabilities under natural selection.
An evolutionary-based algorithm enables modeling of complex solid-solution alloys over exponential search spaces in practical time, accelerating materials design of such high-entropy alloys.