Although the discovery and synthesis of new materials, catalysts, and functional molecules represents the foremost effort that unifies thousands of researchers in physical chemistry, presently characterized compounds represent a minute fraction of chemical space. The highly tunable electronic structure properties of inorganic complexes (i.e., variable spin, oxidation state, and coordination number) make them attractive targets for applications in energy storage, functional materials, and catalysis but present a daunting combinatorial challenge. This vast transition metal compound space cannot be fully enumerated by any traditional Edisonian approach, experimental <i>or </i>computational. In order to advance quantitative structure-activity relationships, reveal emergent phenomena, and accelerate design of materials and catalysts, smarter and faster computational approaches are needed. In this talk, I will outline our efforts over the past few years to accelerate first-principles (i.e., with density functional theory, or DFT) screening of inorganic complexes for catalysis and materials science: i) We have automated and simplified simulation, eliminating the need for tedious preparation of input files or commands by the user, ii) We developed machine learning (ML) models that predict properties from simulation as well as simulation outcome likelihood, and iii) We integrated these tools into an automated design workflow that can make essential decisions on which simulations are best to carry out and why, with awareness of ML model and DFT model uncertainty. Time permitting, I will describe our applications of these tools for advancing understanding in materials synthesis, functional spin crossover materials, and in catalysis.

Predicting properties, learning design rules, and accelerating discovery in inorganic chemistry with computational chemistry

2019 Fall National Meeting

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