We present an automated, open source toolkit for the first-principles screening and discovery of new inorganic molecules and intermolecular complexes. Challenges remain in the automatic generation of candidate inorganic molecule structures due to the high variability in coordination and bonding, which we overcome through a divide-and-conquer tactic that flexibly combines force-field preoptimization of organic fragments with alignment to first-principles-trained metal-ligand distances. Exploration of chemical space is enabled through random generation of ligands and intermolecular complexes from large chemical databases. We validate the generated structures with the root mean squared (RMS) gradients evaluated from density functional theory (DFT), which are around 0.02 Ha/au across a large 150 molecule test set. Comparison of molSimplify results to full optimization with the universal force field reveals that RMS DFT gradients are improved by 40%. Seamless generation of input files, preparation and execution of electronic structure calculations, and post-processing for each generated structure aids interpretation of underlying chemical and energetic trends. © 2016 Wiley Periodicals, Inc.

Zeolites are porous, aluminosilicate materials with many industrial and “green” applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite’s framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 Å 3 , and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies.

Computational high-throughput screening is an essential tool for catalyst design, limited primarily by the efficiency with which accurate predictions can be made. In bulk heterogeneous catalysis, linear free energy relationships (LFERs) have been extensively developed to relate elementary step activation energies, and thus overall catalytic activity, back to the adsorption energies of key intermediates, dramatically reducing the computational cost of screening. The applicability of these LFERs to single-site catalysts remains unclear, owing to the directional, covalent metal-ligand bonds and the broader chemical space of accessible ligand scaffolds. Through a computational screen of nearly 500 model Fe(II) complexes for CH 4 hydroxylation, we observe that 1) tuning ligand field strength yields LFERs by comparably shifting energetics of the metal 3d levels that govern stability of different intermediates and 2) distortion of the metal coordination geometry breaks these LFERs by increasing the splitting between the d xz /d yz and d z 2 metal states that govern reactivity. Thus, in single site catalysts, low Brønsted-Evans-Polanyi slopes for oxo formation, which would limit peak turnover frequency achievable through ligand field tuning alone, can be overcome through structural distortions achievable in experimentally characterized compounds. Observations from this screen also motivate the placement of strong HB donors in targeted positions as a scaffold-agnostic strategy for further activity improvement. More generally, our findings motivate broader variation of coordination geometries in reactivity studies with single-site catalysts.

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 multi-million 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 + 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.

Approximate density functional theory (DFT) suffers from many-electron selfinteraction error, otherwise known as delocalization error, that may be diagnosed and then corrected through elimination of the deviation from exact piecewise linear behavior between integer electron numbers. Although paths to correction of energetic delocalization error are wellestablished, the impact of these corrections on the electron density is less well-studied. Here, we compare the effect on density delocalization of DFT+U, global hybrid tuning, and rangeseparated hybrid tuning on a diverse test set of 32 transition metal complexes and observe the three methods to have qualitatively equivalent effects on the ground state density. Regardless of valence orbital diffuseness (i.e., from 2p to 5p), ligand electronegativity (i.e., from Al to O), basis set (i.e., plane wave versus localized basis set), metal (i.e., Ti, Fe, Ni) and spin state, or tuning method, we consistently observe substantial charge loss at the metal and gain at ligand atoms (ca. 0.3-0.5 e or more). This charge loss at the metal is preferentially from the minority spin, leading to increasing magnetic moment as well. Using accurate wavefunction theory references, we observe that a minimum error in partial charges and magnetic moments occur at higher tuning parameters than typically employed to eliminate energetic delocalization error. These observations motivate the need to develop multi-faceted approximate-DFT error correction approaches that separately treat density delocalization and energetic errors in order to recover both correct density and magnetization properties.