News & Events

Applied Mechanics Colloquia

A Data-Driven Approach to Searching for New Superhard Materials

Taylor Sparks, Associate Professor and Associate Chair of the Materials Science and Engineering Department, University of Utah

Oct 9, 2019
4:00 pm to 5:00 pm Pierce Hall, 209

Over 8 years ago the White House announced the Materials Genome Initiative (MGI) asking computational materials scientists and experimentalists to find ways to “discover, develop, manufacture, and deploy materials twice as fast at a fraction of the cost.” High throughput computation and experiments have made some progress but we are still far from the MGI goal. However, the emerging field of Materials Informatics offers a potentially transformative new approach via machine learning and big data approaches to materials problems. In this talk, I’ll describe the promise, challenges, and opportunities that this new approach affords materials scientists. Specifically, I’ll describe a successful application of new data-driven tools that we have developed for searching for new superhard materials. Machine learning predicted candidate materials Mo0.9W1.1BC and ReWC0.8 are identified, synthesized, and measured. These compounds are then thoroughly characterized using synchrotron diamond anvil diffraction to validate machine learning predictions as well as provide detailed analysis of lattice strain, texture evolution, and deformation mechanisms.

Speaker Bio

Dr. Sparks is an Associate Professor and Associate Chair of the Materials Science and Engineering Department at the University of Utah. He is originally from Utah and an alumni of the department he now teaches in. Before graduate school he worked at Ceramatec Inc. He did his MS in Materials at UCSB and his PhD in Applied Physics at Harvard University in David Clarke’s laboratory and then did a postdoc with Ram Seshadri in the Materials Research Laboratory at UCSB. His current research centers on the discovery, synthesis, characterization, and properties of new materials for energy applications. He is a pioneer in the emerging field of materials informatics whereby big data, data mining, and machine learning are leveraged to solve challenges in materials science. He also hosts a podcast entitled “Materialism” where he discusses the past, present, and future of Materials Science.


David Clarke


Nick Grall