Designing new multicomponent materials, such as corrosion-resistant steels for advanced reactors or turbines, catalytic materials with enhanced selectivity, or new alloys for hydrogen separation, can be a difficult endeavor. The properties of such materials often depend on composition, structure, and the environmental conditions in which they operate. Mapping properties across multiple continuous variables is a complex, combinatorial problem, which makes the development of new materials for engineering applications extremely challenging and expensive. Engineers and scientists have traditionally created maps of material properties across composition, structure, and environmental conditions by making many samples, each of a different composition and/or structure, and then measuring a specific material property of each sample under relevant conditions. Alternatively, simulations of the behavior of these materials have followed a similar approach of modeling one specific composition at a time.
In the November AIChE Journal Perspective article, “High-Throughput Methods Using Composition and Structure Spread Libraries,” John Kitchin and Andrew Gellman of Carnegie Mellon Univ. discuss a method to more efficiently map material properties across composition, structure, and environmental conditions. Their method...
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