In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

A framework for constructing linear free energy relationships to design molecular transition metal catalysts

A computational framework for ligand-driven design of transition metal complexes is presented in this work. We propose a general procedure for the construction of active site-specific linear free energy relationships (LFERs), which are inspired from Hammett and Taft correlations in organic chemistry and grounded in the activation strain model (ASM). Ligand effects are isolated and quantified in terms of their contribution to interaction and strain energy components of ASM. Scalar descriptors that are easily obtainable are then employed to construct the complete LFER. We successfully demonstrate proof-of-concept by constructing and applying an LFER to CH activation with enzyme-inspired [Cu2O2]2+ complexes. The key benefit of using ASM is a built-in compensation or error cancellation between LFER prediction of interaction and strain terms, resulting in accurate barrier predictions for 37 of the 47 catalysts examined in this study. The LFER is also transferable with respect to level of theory and flexible towards the choice of reference system. The absence of interaction-strain compensation or poor model performance for the remaining systems is a consequence of the approximate nature of the chosen interaction energy descriptor and LFER construction of the strain term, which focuses largely on trends in substrate and not catalyst strain.

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts

Catalyst design in enantioselective catalysis has historically been driven by empiricism. In this endeavor, experimentalists attempt to qualitatively identify trends in structure that lead to a desired catalyst function. In this body of work, we lay the groundwork for an improved, alternative workflow that uses quantitative methods to inform decision making at every step of the process. At the outset, we define a library of synthetically accessible permutations of a catalyst scaffold with the philosophy that the library contains every potential catalyst we are willing to make. To represent these chiral molecules, we have developed general 3D representations, which can be calculated for tens of thousands of structures. This defines the total chemical space of a given catalyst scaffold; it is constructed on the basis of catalyst structure only without regard to a specific reaction or mechanism. As such, any algorithmic subset selection method, which is unsupervised (i.e., only considers catalyst structure), should provide an ideal initial screening set for any new reaction that can be catalyzed by that scaffold. Notably, because this design strategy, the same set of catalysts can be used for any reaction that can be catalyzed with that parent catalyst scaffold. These are tested experimentally, and statistical learning tools can be used to create a model relating catalyst structure to catalyst function. Further, this model can be used to predict the performance of each catalyst candidate in the greater database of virtual catalyst candidates. In this way, it is possible estimate the performance of tens of thousands of catalysts by experimentally testing a smaller subset. Using error assessment metrics, it is possible to understand the confidence in new predictions. An experimentalist using this tool can balance the predicted results (reward) with the prediction confidence (risk) when deciding which catalysts to synthesize next in an optimization campaign. These catalysts are synthesized and tested experimentally. At this stage, either the optimization is a success or the predicted values were incorrect and further optimization is required. In the case of the latter, the information can be fed back into the statistical learning model to refine the model, and this iterative process can be used to determine the optimal catalyst. In this body of work, we not only establish this workflow but quantitatively establish how best to execute each step. Herein, we evaluate several 3D molecular representations to determine how best to represent molecules. Several selection protocols are examined to best decide which set of molecules can be used to represent the library of interest. In addition, the number of reactions needed to make accurate, statistical learning models is evaluated. Taken together these components establish a tool ready to progress from the development stage to the utility stage. As such, current research endeavors focus on applying these tools to optimize new reactions.

Enhancing stability by trapping palladium inside N-heterocyclic carbene-functionalized hypercrosslinked polymers for heterogeneous C-C bond formations

Catalyst deactivation caused by the aggregation of active metal species in the reaction process poses great challenges for practical applications of supported metal catalysts in solid-liquid catalysis. Herein, we develop a hypercrosslinked polymer integrated with N-heterocyclic carbene (NHC) as bifunctional support to stabilize palladium in heterogeneous C-C bond formations. This polymer supported palladium catalyst exhibits excellent stability in the one-pot fluorocarbonylation of indoles to four kinds of valuable indole-derived carbonyl compounds in cascade or sequential manner, as well as the representative Suzuki-Miyaura coupling reaction. Investigations on stabilizing effect disclose that this catalyst displays a molecular fence effect in which the coordination of NHC sites and confinement of polymer skeleton contribute together to stabilize the active palladium species in the reaction process. This work provides new insight into the development of supported metal catalysts with high stability and will also boost their efficient applications in advanced synthesis.

Catalyst deactivation caused by the aggregation of active metal species poses great challenges for supported metal catalyzed solid-liquid reactions. Here, the authors develop a hypercrosslinked polymer integrated with N-heterocyclic carbene (NHC) as bifunctional support to stabilize palladium in heterogeneous C-C bond formations.

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

A machine learning exploration of the chemical space surrounding Vaska's complex.

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO₂ reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh₃)₂(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)₂(PPh₃)₂(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H₂, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H₂-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol^–1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol^–1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H₂-activation barrier were identified.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis

Continuous Chirality Measure (CCM) is a computational metric by which to quantify the chirality of a compound. In enantioselective catalysis, prior work has postulated that CCM is correlated to selectivity and can be used to understand which structural features dictate catalyst efficacy. Herein, the investigation of CCM as a metric capable of guiding catalyst optimization is explored. Conformer-dependent CCM is also explored. Finally, CCM is used with Sterimol parameters to significantly improve the performance of Random Forest models.

Designing bifunctional alkene isomerization catalysts using predictive modelling

Optimised isomerisation catalysts are found using an iterative approach combining experimental studies and descriptor modelling.

Effective donor abilities of E-t-Bu and EPh (E = O, S, Se, Te) to a high valent transition metal

Amido rotation in the chromium(vi), d(0)-system NCr(NPr(i)2)2X is under investigation as a method for the parameterization of ligands for their donor properties toward high valent metals. In this study, two new series were prepared and studied based on chalcogenide ligands, X = EBu(t) and EPh and where E = O, S, Se, Te; the OPh and SPh compounds were previously reported. The ligand donor parameters for these ligands correlate with the Cr-E-C angles in these chalcogenide series. In addition, it was found that NBO calculated overlaps and DFT calculated bond dissociation enthalpies correlate within X = halide-, EBu(t)- and EPh-series. All of the new complexes were characterized by X-ray diffraction.

High throughput technology: approaches of research in homogeneous and heterogeneous catalysis

High throughput experimentation (HTE) approaches and the choice of the design of experiment (DoE) tools are discussed with regard to their convenience and applicability in homogeneous and heterogeneous catalysis as a concerted workflow. Much attention is given to diverse methodologies and strategies, which are fundamental for the experimental planning. For two target reactions in two case studies presented in this chapter, HTE methods were applied to create and evaluate catalyst libraries. A homogeneous catalyst case study is illustrated first, which deals with parallel synthesis and screening of organometallic catalysts in the polymerisation of ethylene. The second case study (heterogeneous catalysis) focuses on coherent synthesis and testing of dopant effects on the performance of oxidation catalysts in a reaction of transformation of n-butane to maleic anhydride. Supporting examples from the literature described here show that careful planning of libraries and test conditions is vital in high throughput experimentation in order to deliver meaningful results leading to performance improvements or disruptive new findings.

Evaluation of donor and steric properties of anionic ligands on high valent transition metals

Synthetic protocols and characterization data for a variety of chromium(VI) nitrido compounds of the general formula NCr(NPr(i)(2))(2)X are reported, where X = NPr(i)(2) (1), I (2), Cl (3), Br (4), OTf (5), 1-adamantoxide (6), OSiPh(3) (7), O(2)CPh (8), OBu(t)(F6) (9), OPh (10), O-p-(OMe)C(6)H(4) (11), O-p-(SMe)C(6)H(4) (12), O-p-(Bu(t))C(6)H(4) (13), O-p-(F)C(6)H(4) (14), O-p-(Cl)C(6)H(4) (15), O-p-(CF(3))C(6)H(4) (16), OC(6)F(5) (17), κ(O)-N-oxy-phthalimide (18), SPh (19), OCH(2)Ph (20), NO(3) (21), pyrrolyl (22), 3-C(6)F(5)-pyrrolyl (23), 3-[3,5-(CF(3))(2)C(6)H(3)]pyrrolyl (24), indolyl (25), carbazolyl (26), N(Me)Ph (27), κ(N)-NCO (28), κ(N)-NCS (29), CN (30), NMe(2) (31), F (33). Several different techniques were employed in the syntheses, including nitrogen-atom transfer for the formation of 1. A cationic chromium complex [NCr(NPr(i)(2))(2)(DMAP)]BF(4) (32) was used as an intermediate for the production of 33, which was produced by tin-catalyzed degredation of the salt. Using spin saturation transfer or line shape analysis, the free energy barriers for diisopropylamido rotation were studied. It is proposed that the estimated enthalpic barriers, Ligand Donor Parameters (LDPs), for amido rotation can be used to parametrize the donor abilities of this diverse set of anionic ligands toward transition metal centers in low d-electron counts. The new LDPs do not correlate well to the pK(a) value of X. Conversely, the LDP values of phenoxide ligands do correlate with Hammett parameters for the para-substituents. Literature data for (13)C NMR chemical shifts for a tungsten-based system with various X ligands plotted versus LDP provided a linear fit. In addition, the angular overlap model derived e(σ) + e(π) values for chromium(III) ammine complexes correlate with LDP values. Also discussed is the correlation with XTiCp*(2) spectroscopic data. X-ray diffraction has been used used to characterize 31 of the compounds. From the X-ray diffraction data, steric parameters for the ligands using the Percent Buried Volume and Solid Angle techniques were found.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.