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Bobby Sumpter

Computer Science and Mathematics Divison, ORNL

Biography

Dr. Bobby Sumpter's research is directed primarily toward developing and applying modern computational and mathematical capabilities for the understanding and prediction of chemical and physical processes ranging from the molecular to the nanoscale to full-size engineering applications, using a multidisciplinary approach that integrates chemistry, physics, materials science, mechanical engineering, and biology.  Work is closely coupled with the Nanomaterials Theory Institute at the Center for Nanophase Materials Sciences, where scientific focus is on using theory and multiscale simulations and modeling for providing interpretive and predictive frameworks for virtual design and understanding of novel nanoscale materials with specific and/or emergent properties.  The underlying goal is to understand, predict, design, control, and/or exploit complex behavior that emerges at the nanoscale to enable capabilities that can lead to new innovations and improved materials for energy science and technology.  This vision is possible through a multi-pronged, holistic, and tight integration with Oak Ridge National Laboratory distinctive capabilities in precision experimental synthesis and characterization along side leadership class computing. 

Research

Computational Soft Matter Science: Unraveling the underlying multiscale physicochemical processes that control nanostructure morphologies and macroscopic physical, mechanical, electrical, and transport properties.  Our goal is to understand how to design and control the nanoscale organization of macromolecular nanomaterials and their nanocomposites in order to achieve improved structure, properties, and functionality. Research highlights polymer-based materials for energy storage (supercapacitors and batteries), energy conversion (organic optoelectronics and photovoltaics), and lightweight structural materials (nanocomposites). 

Surface/Substrate-Mediated Interactions, Interfaces, and Self-Assembly: Understanding the mechanism(s) whereby unique assemblies of atoms and molecules are formed under realistic conditions to enable the design and synthesis of materials with prescribed functional (physiochemical) properties.  We combine first-principles discovery and understanding enabled by high fidelity modeling/simulation with application of unique experimental methods for producing materials with nanometer scale structure (synthesis, surface patterning, layer deposition and nanostructuring, etc.)  along with state-of-the-art tools for characterization, to study how intermolecular interactions and the complex correlations of atoms and molecules dictate the formation and properties of oriented nanostructures.  This includes the effects of reduced dimensionality, confinement, and how substrates and support media or the environment interact with and induce changes to materials. 

Nanostructured and Layered Materials: Understanding how atomic scale structure, confinement, and quantum mechanical effects impact electronic processes within these nanostructures and across interfaces.  Very thin sheets of a material can exhibit greatly enhanced properties such as increased electrical conductivity as compared with the bulk and are well suited for applications in new electronic devices, super-strong-light weight composite materials and for energy generation and storage.  For these materials, we can reliably discover/predict structure-function-transport relationships. 

Cyber-Enabled Design of High Capacity Energy Storage Materials: Theory, computational modeling and simulation, to investigate materials electrochemical processes at the length and time scale where the underlying “behavior” is controlled.  The goal is to perform research that will not only lead to predictive simulations but that will advance the basic understanding of energy storage systems.  For example, we want to be able to screen new electrolytes or additives for high-voltage batteries for chemical stability and to design improved formulations based on the insight obtained.  At the same time, we need to be able to model stress buildup during phase transitions in battery electrodes during charge/discharge cycles and to co-design materials and nanostructures to diminish degradation. 

“Virtual” Materials Characterization/Prediction: A computational-based capability using input from on-line experimental tools like X-ray, neutron, HTEM, scanning probes, various spectroscopies, and a purely first principles approach, to enable rapid structural and dynamical characterization: A step towards multi-instrument, multi-physics fusion.  The thesis for this work is that structure and properties of molecules, solids, and liquids are direct reflections of the underlying quantum motion of their electrons, theoretical and computational science when performed in concert with experiments offers great opportunity toward helping solve some of the grand challenges in energy science.  Modern facilities provide a direct means to address this capability, in terms of mathematics, computer science (leadership computing), and best-in-class experimental characterization facilities.