SBIR-STTR Award

Computational Materials Design offers a potential alternative to trial-and-error experimentation in the quest for improved materials. Under this paradigm materials design is driven by input requirements – the desired properties of the material – and result in the output of one or more potential material formulations that can provide the desired properties. The use of relationships between material properties and charge density would better align the process of material design with good engineering practice. A material designer would be able to specify a set of well-defined design inputs: the required properties and associated temperature ranges. Then relationships between properties and quantum mechanical structure would be used to identify novel material formulations that can provide the required properties as the design output. Density functional theory (DFT) asserts that the energy related properties of systems of atoms are functions of the observable electronic charge density. The gradient bundle decomposition method has been used to identify the alloying element to increase the interfacial strength of a carbide-iron alloy by a factor of four using a rational process based on modeling and optimization of quantum interactions between atoms. Quantum computing has tremendous potential to provide exponential speedup for certain classes of computationally challenging problems that are combinatorically complex or involve the simulation of quantum processes. The application of quantum processing to gradient bundle decomposition is expected to provide a speed advantage over conventional computing as the number of useable logical qubits available grows. Approved for Public Release | 22-MDA-11339 (13 Dec 22)