Genetic Algorithms for the Discovery of Homogeneous Catalysts

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts

Toward in silico Catalyst Optimization

In this minireview, we overview a computational pipeline developed within the framework of NCCR Catalysis that can be used to successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions. At the core of this pipeline is the SCINE Molassembler module, a graph-based software that provides algorithms for molecular construction of all periodic table elements. With this pipeline, we are able to simultaneously functionalizenand generate ensembles of transition state conformers, which permits facile exploration of the influencenof various substituents on the overall enantiomeric ratio. This allows preconceived back-of-the-envelope designnmodels to be tested and subsequently refined by providing quick and reliable access to energetically low-lyingntransition states, which represents a key step in undertaking in silico catalyst optimization

Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States

The prediction of reaction selectivity is a challenging task for computational chemistry, not only because many molecules adopt multiple conformations, but also due to the exponential relationship between effective activation energies and rate constants. To account for molecular flexibility, an increasing number of methods exist that generate conformational ensembles of transition state (TS) structures. Typically, these TS ensembles are Boltzmann weighted and used to compute selectivity assuming Curtin-Hammett conditions. This strategy, however, can lead to erroneous predictions if appropriate filtering of the conformer ensembles is not conducted. Here, we demonstrate how any possible selectivity can be obtained by processing the same sets of TS ensembles for a model reaction. To address the burdensome filtering task in a consistent and automated way, we introduce marc, a tool for the modular analysis of representative conformers that aids in avoiding human errors while minimizing the number of reoptimization computations needed to obtain correct reaction selectivity

Automated Prediction of Ground State Spin for Transition Metal Complexes

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2,032 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 97% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl$^-$-THF mixture. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution

Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

The high-throughput molecular exploration and screening of organic electronic materials often starts with either a \u27top-down\u27 mining of existing repositories, or the \u27bottom-up\u27 assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available C(sp2/sp)-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds, which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space. With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process

Engineering Frustrated Lewis Pair Active Sites in Porous Organic Scaffolds for Catalytic CO2 Hydrogenation

Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO2 through a large-scale screening of a diverse library of 25,000 immobilized FLPs. The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a ``top-down" fashion from an experimental database. The chemical and geometrical appropriateness of these LPASs for CO2 hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally-relevant design principles and the leading candidates constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO2

Cost-Informed Bayesian Reaction Optimization

Bayesian optimization (BO) is an increasingly popular method for optimization and development of chemical reactions. Although effective in guiding experimental design, BO does not account for experimentation costs: testing readily available reagents under different conditions might be more cost and time-effective than synthesizing or buying additional ones. To address this issue, we present cost-informed BO (CIBO), an approach tailored for the rational planning of chemical experimentation that prioritizes the most cost-effective experiments. Reagents are used only when their anticipated improvement in reaction performance sufficiently outweighs their costs. Our algorithm tracks the available reagents, including recently acquired ones, and dynamically updates their cost during the optimization. Using literature data of Pd-catalyzed reactions, we show that CIBO reduces the cost of reaction optimization by up to 90% compared to standard BO. Our approach is compatible with any type of cost, e.g., the cost of buying equipment or compounds, waiting time, and environmental or security concerns. We believe CIBO supersedes BO in chemistry and envision applications in both traditional and self-driving laboratories for experiment planning