Virtual high throughput screening,
typically driven by first-principles,
density functional theory calculations, 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 a unified view of our recent developments in
the open source molSimplify code for inorganic discovery. Building
on our previous efforts in the automated generation of highly accurate
inorganic molecular structures, first-principles simulation, and property
analysis to accelerate high-throughput screening, we have recently
incorporated a neural network that both improves structure generation
and predicts electronic properties prior to first-principles calculation.
We also provide an overview of how multimillion molecule organic libraries
can be leveraged 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<sup>+</sup> 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