Optimization-Based Material Discovery

Identification of new materials with desired properties is a holy grail in materials science; recent thrusts in this area, such as the Materials Genome Initiative, aim to speed up the process of discovering materials customized for a specific application using new computational and experimental techniques. In the context of heterogeneous catalysis, this problem is one of identifying a material that can be used as a catalyst which maximizes rate, yield, or selectivity of a desired product in a reaction system. Such problems are computationally and experimentally challenging owing to the large space of potential compounds needs to be explored.

The first step in materials discovery is to determine the target characteristics of the material that will lead to maximizing its performance objective. For catalysis, this means identifying the values of kinetics of reactions and thermochemistry of species in the reaction system for which the overall predicted yield/rate/selectivity (or any other user-defined performance criteria) is maximized. Once the targets are identified, computational chemistry (specifically density functional theory, or DFT) calculations and/or experimental analyes can be used to identify the materials with these target characterisitcs.

We formulate this step as a nonlinear optimization problem that maximizes a chosen performance criterion subject to (non)linear physico-chemical, thermodynamic, and material constraints. Such problems tend to involve stiff systems of equations and the number of decision variables increase exponentially with the size of the reaction network or the complexity of the catalyst. We leverage the recent advances in global optimization algorithms and develop novel reformulation strategies to solve such problems quickly and robustly. In addition, we are also developing methodologies to identify the few “key” characteristic parameters that determine the performance of the material, thereby reducing the dimensionality of the materials space.

Our group is collaborating with experts in the area of heterogeneous catalysis to develop a three pronged approach shown in the figure, comprising of: (i) density functional theory (DFT) calculations to obtain microscopic properties of the reaction system such as the thermochemistry and kinetics, (ii) experimental studies to test/measure the overall performance of the catalyst, and (iii) optimization to link microscopic parameters to optimal values of macroscopic observables. New software tools are being concurrently developed for catalyst discovery and we are currently pursuing applications in the domain of CO2 upgrading, biomass conversion, and methane processing.

A workflow diagram for the proposed integrated computational-experimental approach for identifying new catalysts.