NSF Award
1928882
NONTECHNICAL SUMMARY<br/>This award receives funds from the Division of Materials Research, the Chemistry Division and the Office of Advanced Cyberinfrastructure. This award supports research and education that uses data-centric methods to enable the prediction of metal oxide compounds with desired properties. Organically-templated metal oxides have a tremendous degree of structural diversity and compositional flexibility. This allows chemists to tune the structures, properties, and symmetries of these compounds to optimize their performance in specific applications that include catalysis, molecular sieving, gas adsorption, and nonlinear optics. However, new compounds are typically created by a trial-and-error procedure, and creating novel compounds with specific structures is a grand challenge in solid state chemistry. This project will develop artificial intelligence techniques for computers called machine learning techniques that can be used to predict the conditions for chemical reactions that will increase structural diversity and lead to specific structural features. This project will also develop machine learning techniques that generate human-readable explanations about the formation mechanism, which will be tested in the laboratory.<br/> <br/>The primary impact of this project will be to decrease the amount of time and to lower the cost of discovering new materials with specific structural features, which in turn help bring new materials for applications to market more quickly. This project is an example of a collaboration among synthetic chemists, computational chemists, and computer scientists and as a model it may be directly transferred to a wide range of disciplines and avenues of investigation. Undergraduate student research opportunities and curricular developments will be involved throughout the project, thus contributing to the scientific workforce.<br/> <br/>TECHNICAL SUMMARY <br/>This award receives funds from the Division of Materials Research, the Chemistry Division and the Office of Advanced Cyberinfrastructure. This award supports research and education that uses data-centric methods to enable the prediction of metal oxide compounds with desired properties. Hydrothermal synthesis is widely used to create new metal oxide materials with a wide range of functional properties and applications. This project will advance the field by developing software infrastructure for associating the results of X-ray diffraction experiments with individual reactions, extracting structural outcome descriptors from this data, and then determining the extent to which these structural outcomes can be predicted from reaction description data. This will be achieved by developing structural outcome descriptors for geometric properties, non-covalent interaction properties, and electron-density properties, then building machine learning models that correlate these outcomes to reaction conditions, and finally testing the quality of these predictions experimentally. Active learning and auditable and interpretable models will be incorporated into the workflows to help synthetic chemists select better (more insightful/novel) reactions in an interactive fashion.
