Inorganic discovery & machine learning

Virtual high throughput screening has emerged as a powerful tool for the discovery of new materials. Although the computational materials science community has benefited from open source tools for the rapid structure generation, calculation, and analysis of crystalline inorganic materials, software and strategies to address the unique challenges of inorganic complex discovery have not been as widely available. We present our recent developments in the open source molSimplify code for inorganic discovery. This overview includes our automated generation of highly accurate inorganic molecular structures, first-principles simulation, and property analysis to accelerate high-throughput screening. We have also recently incorporated a neural network that both improves structure generation and predicts electronic properties prior to first-principles calculation. We leverage multimillion molecule organic libraries for inorganic discovery alongside cheminformatics concepts of molecular diversity in order to efficiently traverse chemical space. We demonstrate all of these tools on the discovery of design rules for octahedral Fe(II/III) redox couples with nitrogen ligands. Over a search of only approximately 40 new molecules, we obtain redox potentials relative to the Fc/Fc+ couple ranging from −1 to 4.5 V in aqueous solution. Our new automated correlation analysis reveals heteroatom identity and the degree of structural branching to be key ligand descriptors in determining redox potential. This inorganic discovery toolkit provides a promising approach to advancing transition metal complex design beyond traditional approaches.

Check out our invited article, part of I&ECR's "2017 Class of Influential Researchers" special issue here. Also check out the recent work from our group that this paper highlights: on using large ligand libraries (Kim et al., Chem. Mater. 2017), discovering new functional chemistry from screening (Gani et al., Chem. Mater. 2016), training neural networks to predict transition metal complex properties (Janet et al., arXiv), and the original molSimplify tool (Ioannidis et al., J. Comput. Chem. 2016, or check out our molSimplify tutorials here).

About Us

The Kulik group focuses on the development and application of new electronic structure methods and atomistic simulations tools in the broad area of catalysis.

Our Interests

We are interested in transition metal chemistry, with applications from biological systems (i.e. enzymes) to nonbiological applications in surface science and molecular catalysis.

Our Focus

A key focus of our group is to understand mechanistic features of complex catalysts and to facilitate and develop tools for computationally driven design.

Contact Us

Questions or comments? Let us know! Contact Dr. Kulik: