This CAREER award supports the development and application of machine learning and data mining methods to predict the surface structures of crystalline materials in a variety of chemical environments. The PI will develop a three-step process which is designed to minimize the computational expense of predicting surface structures by maximizing the re-use of existing data. In the first step, evolutionary algorithms will be used to develop a re-usable library of likely surface reconstructions for bulk structure types. In the second step a combination of evolutionary algorithms and data mining methods will be developed to determine the most likely surface structures for a particular material surface. In the third step, ab-initio calculations and cluster expansions will be used to identify the particular surface structures with the lowest energy. The structure prediction process will be developed, validated, and applied to three technologically important systems: perovskite-structured oxides, Au-Pd alloys, and spinel-structured oxides.
The research will be integrated with an educational outreach program that is designed to strengthen the pipeline of researchers who have both the interest and ability to discover and design new materials through computational research. At the elementary school level, the PI has volunteered to partner with a master teacher at a majority-minority, low-income Baltimore City public school to share scientific knowledge, help construct an effective curriculum, and design a hands-on exercise intended to educate and excite students about STEM activities. At the middle school level, the PI will teach computer programming skills to Baltimore City students who are participating in a VEX robotics competition. At the high school level, a female student from a nearby high school will participate in the research project as member of the research team. The PI will work with the graduate student to develop an online tutorial that covers fundamental topics in materials surface science, and elements of this tutorial will be integrated into the core curriculum of the Department of Materials Science and Engineering at Johns Hopkins University.
NONTECHNICAL SUMMARY
This CAREER award supports the development and application of advanced computational and data mining methods to predict how atoms are arranged on the surfaces of materials. The ability to use computers to predict the properties of material surfaces will facilitate the design of new materials for a wide range of technologies including batteries, catalysts, and sensors. However before a property of a surface can be predicted, it is first necessary to predict the atomic structure, or how the atoms are arranged, on the surface. The PI will address this challenging problem by developing a method to accurately predict material surface structures with low computational cost. This will be accomplished by combining a variety of computational tools in a way that leverages existing knowledge about the surface structures to predict the surface structure of a new material. The method developed in this research will be used to predict the surface structures of three representative classes of materials that were chosen for their importance in technologies such as batteries and catalysts.
The research will be integrated with an educational outreach program that is designed to strengthen the pipeline of researchers who have both the interest and ability to use computers to discover and design new materials. At the elementary school level, the PI has volunteered to partner with a master teacher at a majority-minority, low-income Baltimore City public school to share scientific knowledge, help construct an effective curriculum, and design a hands-on exercise intended to educate and excite students about science and engineering. At the middle school level, the PI will teach computer programming skills to Baltimore City students who are participating in a robotics competition. At the high school level, a female student from a nearby high school will participate in the research project as member of the research team. The PI will work with the graduate student to develop an online tutorial that covers fundamental topics in materials surface science, and elements of this tutorial will be integrated into the core curriculum of the Department of Materials Science and Engineering at Johns Hopkins University.
