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David Sholl, Ph.D.

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Date(s) - 10/28/2014
4:00 pm - 5:00 pm

622 Min H. Kao Electrical Engineering and Computer Science Building


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David Sholl, Ph.D.

School of Chemical and Biomedical Engineering

Georgia Institute of Technology

Atlanta, GA, USA




Dr. David Sholl is the Michael E. Tennenbaum Family Chair, GRA Eminent Scholar in Energy Sustainability, and Department Chair of the School of Chemical & Biomolecular Engineering at Georgia Tech. He has held this position since January 2008. Prior to his appointment at Georgia Tech, Dr. Sholl was on the faculty at Carnegie Mellon University for 10 years. Dr. Sholl’s research uses computational materials modeling to accelerate development
of new materials for energy-related applications, including generation and storage of gaseous and liquid fuels and carbon dioxide mitigation. He has published over 180 peer-reviewed papers. He has also written a textbook on Density Functional Theory, a quantum chemistry method that is
widely applied through the physical sciences and engineering. Dr. Sholl is a Senior Editor of the ACS journal Langmuir, and is an Associate Director of Georgia Tech’s Strategic Energy Institute. More information on Dr. Sholl’s research group is available from



“Using High Throughput Computation to Accelerate Development of Materials for Scalable Energy Technologies”

Computational modeling of materials can be a powerful complement to experimental methods when models with useful levels of predictive ability can be deployed more rapidly than experiments. Achieving this goal involves judicious choices about the level of modeling that is used and the key physical properties of the materials of interest that control performance
in practical applications. Dr. Scholl will discuss two examples of using high throughput computations to identify new materials for scalable energy applications: the use of metal-organic frameworks in membranes and gas storage and the selection of metal hydrides for high temperature nuclear applications. These examples highlight the challenges of generating su ciently comprehensive material libraries and the potential advantages and di culties of using computational methods to examine large libraries of materials.

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