The goal of our research is to transform the discovery of new materials and chemicals by creatively combining molecular modeling techniques, metaheuristic sampling, and machine learning methods. We aim to develop tools for materials discovery that can be used in many different applications and, in line with this goal, members of our lab are involved in projects that encompass applications ranging from catalyst design, developing novel biomimetic polymers, discovering “green” surfactants for CO2/water systems (with applications both in green chemistry, oil recovery, and carbon sequestration), finding treatments to prevent calcium pyrophosphate deposition disease (a common cause of rheumatoid arthritis), discovering new polymeric materials with extraordinary mechanical properties, developing oxygen conductors for chemical looping combustion, and finding enhanced methods for microprocessor manufacturing through selective atomic layer deposition. In spite of the apparent disparity of problems, there is an underlying common theme in all of them: the goal of the project is to discover a new material, and computer simulations have the ability to accelerate this search and guide experiments that would otherwise be very expensive and time-consuming.
We believe the field of molecular modeling is now at a turning point, where it has moved from being a tool to confirm or interpret experimental data, to being capable of taking the lead in the chemical discovery process, and we are very excited for the opportunity to help bring about this change.
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