Computational Mapping of Dirhodium(II) Catalysts

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools

Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C-H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold

Exploration of the local chemical space of molecular scaffolds by post-functionalization (PF) is a promising route to discover novel molecules with desired structure and function. PF with rationally chosen substituents based on known electronic and steric properties is a commonly used experimental and computational strategy in screening, design and optimization of catalytic scaffolds. Automated generation of reasonably accurate geometric representations of post-functionalized molecular scaffolds is highly desirable for data-driven applications. However, automated PF of transition metal (TM) complexes remains challenging. In this work a Python-based workflow, ChemSpaX, that is aimed at automating the PF of a given molecular scaffold with special emphasis on TM complexes, is introduced. In three representative applications of ChemSpaX by comparing with DFT and DFT-B calculations, we show that the generated structures have a reasonable quality for use in computational screening applications. Furthermore, we show that ChemSpaX generated geometries can be used in machine learning applications to accurately predict DFT computed HOMO-LUMO gaps for transition metal complexes. ChemSpaX is open-source and aims to bolster and democratize the efforts of the scientific community towards data-driven chemical discovery.

Melding of Experiment and Theory Illuminates Mechanisms of Metal-Catalyzed Rearrangements: Computational Approaches and Caveats

This review summarizes approaches and caveats in computational modeling of transition-metal-catalyzed sigmatropic rearrangements involving carbene transfer. We highlight contemporary examples of combined synthetic and theoretical investigations that showcase the synergy achievable by integrating experiment and theory.

1 Introduction

2 Mechanistic Models

3 Theoretical Approaches and Caveats

3.1 Recommended Computational Tools

3.2 Choice of Functional and Basis Set

3.3 Conformations and Ligand-Binding Modes

3.4 Solvation

4 Synergy of Experiment and Theory – Case Studies

4.1 Metal-Bound or Free Ylides?

4.2 Conformations and Ligand-Binding Modes of Paddlewheel Complexes

4.3 No Metal, Just Light

4.4 How To ‘Cope’ with Nonstatistical Dynamic Effects

5 Outlook

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry-it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a "big data" view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.