(415e) New Computational Tools for High-Throughput Discovery in Transition Metal Catalysis

Virtual high throughput screening has emerged as a powerful tool for the discovery of new materials. Nevertheless, screening of inorganic complexes is presently limited by the high cost of accurate property evaluation and the large, complex search space. To address these challenges, we have recently developed molSimplify¹, an open-source toolkit for the automated generation of highly accurate inorganic molecular structures and property analysis to accelerate high throughput screening of inorganic complexes. In conjunction with our structure generation toolkit, we have also developed and implemented a robust and automated workflow involving: (1) selection of a test set of ligands maximizing relative molecular diversity², (2) prediction of electronic structure outputs with a neural network prior to simulation for pre-screening and (3) screening of multi-million molecule organic libraries according to the discovered design rules to discover experimentally accessible ligand candidates². We demonstrate the broad utility of our integrated approach to inorganic complex discovery across the following applications: design of redox active functionalized ferrocenium complexes for selective ion separation³, discovery of octahedral Fe(II/III) redox couples with nitrogen ligands⁴, and design of single-site Fe(II) catalysts for oxygen activation. We also demonstrate the potential for molSimplify to accelerate catalytic property evaluation by automatically generating high-quality transition state guesses for common reactions in inorganic chemistry.

¹E. I. Ioannidis, T. Z. H. Gani, and H. J. Kulik “molSimplify: A toolkit for automating discovery in inorganic chemistry” J. Comput. Chem. 37, 2106-2117 (2016).

²J. Y. Kim, A. H. Steeves, and H. J. Kulik “Harnessing Organic Ligand Libraries for First-Principles Inorganic Discovery: Indium Phosphide Quantum Dot Precursor Design Strategies” Chem. Mater., just accepted (2017).

³T. Z. H. Gani, E. I. Ioannidis, and H. J. Kulik “Computational Discovery of Hydrogen Bond Design Rules for Electrochemical Ion Separation” Chem. Mater., 28 6207-6218 (2016).

⁴J.P. Janet, T. Z. H. Gani, A. H. Steeves, E. I. Ioannidis, and H. J. Kulik “Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design” Ind. Eng. Chem. Res., just accepted (2017).