Bryan Goldsmith is an Assistant Professor in the Department of Chemical Engineering. His works focus on the development of novel catalysts and materials. The world is facing a growing population, mass consumerism, and rising greenhouse gas levels, all the while people strive to increase their standard of living. Computational modeling of catalysts and materials, and making use of its synergy with experiments, facilitates the process to design new systems since it provides a valuable way to test hypotheses and understand design criteria. His research team focuses on obtaining a deep understanding of catalytic systems and advanced materials for use in sustainable chemical production, pollution abatement, and energy generation. They use first-principles modeling (e.g., density-functional theory and wave function based methods), molecular simulation, and data analytics tools (e.g., statistical learning and data mining) to extract key insights of catalysts and materials under realistic conditions, and to help create a platform for their design.
Phani Motamarri is an Assistant Research Scientist in the department of Mechanical Engineering. His research interests lie in the broad scope of computational materials science with emphasis on computational nano-science leading to applications in the areas of mechanics of materials and energy. His research is strongly multidisciplinary, drawing ideas from applied mathematics, data science, quantum-mechanics, solid-mechanics, materials science and scientific computing.
The current research focus lies in developing systematically improvable real-space computational methodologies and associated mathematical techniques for conducting large-scale electronic-structure (ab-initio) calculations -via- density functional theory (DFT). Massively parallel and scalable numerical algorithms using finite-elements (DFT-FE) are developed as a part of this research effort, which enabled large-scale DFT calculations on tens of thousands of atoms for the first time using finite-element basis. These computational methods will aid fundamental studies on defects in materials, molecular and nanoscale systems which otherwise would have been difficult to study with the existing state of the art computational methods. Current areas of application include — (a) first-principles modelling of energetics of point defects and dislocations in Al, Mg and its alloys which are popular in light-weighting applications to provide useful inputs to meso-scale and continuum models, (b) providing all-electron DFT input to advanced electronic structure approaches like the GW method for accurate prediction of electronic properties in semiconductor-materials.
Santiago Schnell’s lab combines chemical kinetics, molecular modeling, biochemical measurements and computational modeling to build a comprehensive understanding of proteostasis and protein forlding diseases. They also investigate other complex physiological systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole.
Charles Doering is the Nicholas D. Kazarinoff Collegiate Professor of Complex Systems, Mathematics and Physics and the Director of the Center for the Study of Complex Systems. He is a Fellow of the American Physical Society, and a Fellow of the Society of Industrial and Applied Mathematics (SIAM). He uses stochastic, dynamical systems arising in biology, chemistry and physics models, as well as systems of nonlinear partial differential equations to extract reliable, rigorous and useful predictions. His research spans rigorous estimation, numerical simulations and abstract functional and probabilistic analysis.
Don Siegel is an Associate Professor affiliated with the Mechanical Engineering Department and the Department of Material Science and Engineering. His research targets the discovery, characterization, and understanding of novel materials for energy-related applications. These efforts primarily employ atomic scale modeling to predict thermodynamic properties and kinetics. These data provide the necessary ingredients for identifying performance limiting mechanisms and for the “virtual screening” of candidate compounds having desired properties. Prof. Siegel is currently exploring several varieties of energy storage materials, lightweight structural alloys, and materials suitable for use in carbon capture applications.
His research focuses on understanding the role of strong correction effects in many-body quantum systems. The objective is to discover novel quantum states/materials and to understand their exotic properties using theoretical/numerical methods (with emphasis on topological properties). In his research, numerical techniques are applied to resolve the fate of a quantum material (or a theoretical model) in the presence of multiple competing ground states and to provide quantitative guidance for further (experimental/theoretical) investigations.
Professor Becker leads an interdisciplinary group to understand problems in surface geochemistry and computational mineralogy, thus there are research opportunities in fields ranging from experimental approaches and computational modeling of actinide geochemistry (U immobilization in the environment, actinide-containing solids under extreme pressure, temperature, and radiation, U/Np/Pu redox processes) to carbonate biomineralization. Other research includes calculating redox processes (including resolving individual kinetic barriers that control kinetics) carbonate and phosphate biomineralization (from environmental applications to processes on teeth). As a part of Mineralogy and Materials Science Research Group, Becker’s group interacts with Radiation Effects and Radioactive Waste Management group, Michigan Geomicrobiology group, Electron Microbeam Analysis Laboratory (EMAL) and Mineral Physics group.
The Linderman group works in the area of computational biology, especially in developing multi-scale models that link molecular, cellular and tissue level events. Current areas of focus include: (1) hybrid multi-scale agent-based modeling to simulate the immune response to Mycobacterium tuberculosis and identify potential therapies, (2) models of signal transduction, particularly for G-protein coupled receptors, and (3) multi-scale agent-based models of cancer cell chemotaxis.
C. David Remy is an Assistant Professor of Mechanical Engineering, and head of the Robotics and Motion Laboratory. The lab seeks to systematically exploit mechanical dynamics to make future robots faster, more efficient, and more agile. Inspired by nature, the group designs and controls robots whose motion emerges in great part passively from the interaction of inertia, gravity, and elastic oscillations, and is merely initiated and shaped through active actuator inputs. In the long term vision, the lab’s research will allow the development of systems that reach and even exceed the agility of humans and animals. It will enable us to build autonomous robots that can run as fast as a cheetah and as enduring as a husky, while mastering the same terrain as a mountain goat. To this end, the group will develop appropriate methods for the control and design of robots. It will draw inspiration from biomechanics and biology, deepen our theoretical understanding of natural dynamics through simulation, and employ advanced numerical optimization as primary tool for systematic design and development.
Professor Ziff carries out computational and theoretical studies of various physical problems, most notably percolation but also catalysis modeling and several reaction/diffusion systems. For percolation, he has developed various algorithms that have allowed substantial increases in performance, for the study of threshold behavior, crossing probability, etc. He also studies algorithms for efficiently simulating rare-event simulations such as chemical reactions and diffusion-limited aggregation.