The Genesis of Molecular Volcano Plots

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Efficiency Conceptualization Model: A Theoretical Method for Predicting the Turnover of Catalysts

In recent times, the theoretical prediction of catalytic efficiency is of utmost urgency. With the advent of density functional theory (DFT), reliable computations can delineate a quantitative aspect of the study. To this state-of-the-art approach, valuable incorporation would be a tool that can acknowledge the efficiency of a catalyst. In the current work, we developed the efficiency conceptualization model (ECM) that utilizes the quantum mechanical tool to achieve efficiency in terms of turnover frequency (TOF). Twenty-six experimentally designed transition metal (TM) water oxidation catalysts were chosen under similar experimental conditions of temperature, pressure, and pH to execute the same. The computations conclude that the Fe-based [Fe(OTf)2(Me2Pytacn)] (MWOC-17) is a highly active catalyst and, therefore, can endure for more time in the catalytic cycle. Our results conclude that the Ir-based catalysts [Cp*Ir(κ2-N,O)X] with MWOC-23: X=Cl; and MWOC-24: X=NO3 report the highest computed turnover numbers (TONs),τ c o m p u t e d T O N 0 ${\tau _{computed\;TON}^0 }$ of 406 and 490 against the highest experimental TONs,τ e x p e r i m e n t a l T O N ${\tau _{experimental\;TON} }$ of 1200 and 2000 respectively, whereas the Co-based [Co(12-TMC)]2+ (MWOC-19) has the lowest TONs (τ c o m p u t e d T O N 0 ${\tau _{computed\;TON}^0 }$ =19, τexperimental TON=16) among the chosen catalysts and thereby successful in corroborating the previous experimental results.

Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions

Molecular volcano plots, which facilitate the rapid prediction of the activity and selectivity of prospective catalysts, have emerged as powerful tools for computational catalysis. Here, we integrate microkinetic modeling into the volcano plot framework to develop "microkinetic molecular volcano plots". The resulting unified computational framework allows the influence of important reaction parameters, including temperature, reaction time, and concentration, to be quickly incorporated and more complex situations, such as off-cycle resting states and coupled catalytic cycles, to be tackled. Compared to previous generations of molecular volcanoes, these microkinetic counterparts offer a more comprehensive understanding of catalytic behavior, in which selectivity and product ratios can be explicitly determined by tracking the evolution of each product concentration over time. This is demonstrated by examining two case studies, rhodium-catalyzed hydroformylation and metal-catalyzed hydrosilylation, in which the unique insights provided by microkinetic modeling, as well as the ability to simultaneously screen catalysts and reaction conditions, are highlighted. To facilitate the construction of these plots/maps, we introduce mikimo, a Python program that seamlessly integrates with our previously developed automated volcano builder, volcanic.

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.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

2D Monolayer Catalysts: Towards Efficient Water Splitting and Green Hydrogen Production

A viable alternative to non-renewable hydrocarbon fuels is hydrogen gas, created using a safe, environmentally friendly process like water splitting. An important role in water-splitting applications is played by the development of two-dimensional (2D) layered transition metal chalcogenides (TMDCs), transition metal carbides (MXenes), graphene-derived 2D layered nanomaterials, phosphorene, and hexagonal boron nitride. Advanced synthesis methods and characterization instruments enabled an effective application for improved electrocatalytic water splitting and sustainable hydrogen production. Enhancing active sites, modifying the phase and electronic structure, adding conductive elements like transition metals, forming heterostructures, altering the defect state, etc., can improve the catalytic activity of 2D stacked hybrid monolayer nanomaterials. The majority of global research and development is focused on finding safer substitutes for petrochemical fuels, and this review summarizes recent advancements in the field of 2D monolayer nanomaterials in water splitting for industrial-scale green hydrogen production and fuel cell applications.

Thermodynamic and Kinetic Activity Descriptors for the Catalytic Hydrogenation of Ketones

Activity descriptors are a powerful tool for the design of catalysts that can efficiently utilize H2 with minimal energy losses. In this study, we develop the use of hydricity and H- self-exchange rates as thermodynamic and kinetic descriptors for the hydrogenation of ketones by molecular catalysts. Two complexes with known hydricity, HRh(dmpe)2 and HCo(dmpe)2, were investigated for the catalytic hydrogenation of ketones under mild conditions (1.5 atm and 25 °C). The rhodium catalyst proved to be an efficient catalyst for a wide range of ketones, whereas the cobalt catalyst could only hydrogenate electron-deficient ketones. Using a combination of experiment and electronic structure theory, thermodynamic hydricity values were established for 46 alkoxide/ketone pairs in both acetonitrile and tetrahydrofuran solvents. Through comparison of the hydricities of the catalysts and substrates, it was determined that catalysis was observed only for catalyst/ketone pairs with an exergonic H- transfer step. Mechanistic studies revealed that H- transfer was the rate-limiting step for catalysis, allowing for the experimental and computation construction of linear free-energy relationships (LFERs) for H- transfer. Further analysis revealed that the LFERs could be reproduced using Marcus theory, in which the H- self-exchange rates for the HRh/Rh+ and ketone/alkoxide pairs were used to predict the experimentally measured catalytic barriers within 2 kcal mol-1. These studies significantly expand the scope of catalytic reactions that can be analyzed with a thermodynamic hydricity descriptor and firmly establish Marcus theory as a valid approach to develop kinetic descriptors for designing catalysts for H- transfer reactions.

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target

A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.

Ratcheting synthesis

Synthetic chemistry has traditionally relied on reactions between reactants of high chemical potential and transformations that proceed energetically downhill to either a global or local minimum (thermodynamic or kinetic control). Catalysts can be used to manipulate kinetic control, lowering activation energies to influence reaction outcomes. However, such chemistry is still constrained by the shape of one-dimensional reaction coordinates. Coupling synthesis to an orthogonal energy input can allow ratcheting of chemical reaction outcomes, reminiscent of the ways that molecular machines ratchet random thermal motion to bias conformational dynamics. This fundamentally distinct approach to synthesis allows multi-dimensional potential energy surfaces to be navigated, enabling reaction outcomes that cannot be achieved under conventional kinetic or thermodynamic control. In this Review, we discuss how ratcheted synthesis is ubiquitous throughout biology and consider how chemists might harness ratchet mechanisms to accelerate catalysis, drive chemical reactions uphill and programme complex reaction sequences.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

Optimizing Eg Orbital Occupancy of Transition Metal Sulfides by Building Internal Electric Fields to Adjust the Adsorption of Oxygenated Intermediates for Li-O2 Batteries

Li-O2 batteries are acknowledged as one of the most promising energy systems due to their high energy density approaching that of gasoline, but the poor battery efficiency and unstable cycling performance still hinder their practical application. In this work, hierarchical NiS2 -MoS2 heterostructured nanorods are designed and successfully synthesized, and it is found that heterostructure interfaces with internal electric fields between NiS2 and MoS2 optimized eg orbital occupancy, effectively adjusting the adsorption of oxygenated intermediates to accelerate reaction kinetics of oxygen evolution reaction and oxygen reduction reaction. Structure characterizations coupled with density functional theory calculations reveal that highly electronegative Mo atoms on NiS2 -MoS2 catalyst can capture more eg electrons from Ni atoms, and induce lower eg occupancy enabling moderate adsorption strength toward oxygenated intermediates. It is evident that hierarchical NiS2 -MoS2 nanostructure with fancy built-in electric fields significantly boosted formation and decomposition of Li2 O2 during cycling, which contributed to large specific capacities of 16528/16471 mAh g-1 with 99.65% coulombic efficiency and excellent cycling stability of 450 cycles at 1000 mA g-1 . This innovative heterostructure construction provides a reliable strategy to rationally design transition metal sulfides by optimizing eg orbital occupancy and modulating adsorption toward oxygenated intermediates for efficient rechargeable Li-O2 batteries.

Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions

The need for more efficient catalytic processes is ever-growing, and so are the costs associated with experimentally searching chemical space to find new promising catalysts. Despite the consolidated use of density functional theory (DFT) and other atomistic models for virtually screening molecules based on their simulated performance, data-driven approaches are rising as indispensable tools for designing and improving catalytic processes. Here, we present a deep learning model capable of generating new catalyst-ligand candidates by self-learning meaningful structural features solely from their language representation and computed binding energies. We train a recurrent neural network-based Variational Autoencoder (VAE) to compress the molecular representation of the catalyst into a lower dimensional latent space, in which a feed-forward neural network predicts the corresponding binding energy to be used as the optimization function. The outcome of the optimization in the latent space is then reconstructed back into the original molecular representation. These trained models achieve state-of-the-art predictive performances in catalysts' binding energy prediction and catalysts' design, with a mean absolute error of 2.42 kcal mol^-1 and an ability to generate 84% valid and novel catalysts.

A Ternary Composite with Medium Adsorption Confirms Good Reversibility of Li-Se Batteries

For Li-Se batteries, cathode using carbonaceous hosts to accommodate Se performed modestly, whereas those applying metallic compounds with stronger chemical adsorption exhibited even more rapid capacity decay, the intrinsic reasons for which are still not clear. Herein, it is found that Se tends to precipitate on the surface of the electrode during cycling, and the precipitation speed depends on the polarization degree of the host. A further enhanced adsorption does not certainly generate better electrochemical activity, since hosts with overhigh adsorption ability are hard to desorb polyselenides, leading to catalyst passivation and rapid capacity decay. These findings encourage us to design a ternary anatase/rutile/titanium nitride (aTiO₂ /rTiO₂ /TiN@C) composite host, integrating good adsorption of TiO₂ and rapid electron transport ability of TiN, and introducing rutile to weaken overall adsorption. The aTiO₂ /rTiO₂ /TiN@C composite with medium adsorption not only avoids rapid loss of active substances in electrolyte but also slows down the precipitation speed of Se. As a result, the aTiO₂ /rTiO₂ /TiN@C/Se electrode delivered good rate capability(154 mA h g^-1 at 20 C) and good cycling stability(a low decay of 0.024% per cycle within 500 cycles at 2 C).

The ABC of Generalized Coordination Numbers and Their Use as a Descriptor in Electrocatalysis

The quest for enhanced electrocatalysts can be boosted by descriptor-based analyses. Because adsorption energies are the most common descriptors, electrocatalyst design is largely based on brute-force routines that comb materials databases until an energetic criterion is verified. In this review, it is shown that an alternative is provided by generalized coordination numbers (denoted by CN ¯ $\overline {{\rm{CN}}} $ or GCN), an inexpensive geometric descriptor for strained and unstrained transition metals and some alloys. CN ¯ $\overline {{\rm{CN}}} $ captures trends in adsorption energies on both extended surfaces and nanoparticles and is used to elaborate structure-sensitive electrocatalytic activity plots and selectivity maps. Importantly, CN ¯ $\overline {{\rm{CN}}} $ outlines the geometric configuration of the active sites, thereby enabling an atom-by-atom design, which is not possible using energetic descriptors. Specific examples for various adsorbates (e.g., *OH, *OOH, *CO, and *H), metals (e.g., Pt and Cu), and electrocatalytic reactions (e.g., O₂  reduction, H₂  evolution, CO oxidation, and reduction) are presented, and comparisons are made against other descriptors.

Theory-guided development of homogeneous catalysts for the reduction of CO2 to formate, formaldehyde, and methanol derivatives

The stepwise catalytic reduction of carbon dioxide (CO₂) to formic acid, formaldehyde, and methanol opens non-fossil pathways to important platform chemicals. The present article aims at identifying molecular control parameters to steer the selectivity to the three distinct reduction levels using organometallic catalysts of earth-abundant first-row metals. A linear scaling relationship was developed to map the intrinsic reactivity of 3d transition metal pincer complexes to their activity and selectivity in CO₂ hydrosilylation. The hydride affinity of the catalysts was used as a descriptor to predict activity/selectivity trends in a composite volcano picture, and the outstanding properties of cobalt complexes bearing bis(phosphino)triazine PNP-type pincer ligands to reach the three reduction levels selectively under different reaction conditions could thus be rationalized. The implications of the composite volcano picture were successfully experimentally validated with selected catalysts, and the challenging intermediate level of formaldehyde could be accessed in over 80% yield with the cobalt complex 6. The results underpin the potential of tandem computational-experimental approaches to propel catalyst design for CO₂-based chemical transformations.

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis

Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Engineering 3d-2p-4f Gradient Orbital Coupling to Enhance Electrocatalytic Oxygen Reduction

The development of highly efficient and economical materials for the oxygen reduction reaction (ORR) plays a key role in practical energy conversion technologies. However, the intrinsic scaling relations exert thermodynamic inhibition on realizing highly active ORR electrocatalysts. Herein, a novel and feasible gradient orbital coupling strategy for tuning the ORR performance through the construction of Co 3d-O 2p-Eu 4f unit sites on the Eu₂ O₃ -Co model is proposed. Through the gradient orbital coupling, the pristine ionic property between Eu and O atoms is assigned with increased covalency, which optimizes the e_g occupancy of Co sites, and weakens the OO bond, thus ultimately breaking the scaling relation between *OOH and *OH at Co-O-Eu unit sites. The optimized model catalyst displays onset and half-wave potential of 1.007 and 0.887 V versus reversible hydrogen electrode, respectively, which are higher than those of commercial Pt/C and most Co-based catalysts ever reported. In addition, the catalyst is found to possess superior selectivity and durability. It also reveals better cell performance than commercial noble-metal catalysts in Zn-air batteries in terms of high power/energy densities and long cycle life. This study provides a new perspective for electronic modulation strategy by the construction of gradient 3d-2p-4f orbital coupling.

Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes

Volcano plots and activity maps are powerful tools for studying homogeneous catalysis. Once constructed, they can be used to estimate and predict the performance of a catalyst from one or more descriptor variables. The relevance and utility of these tools has been demonstrated in several areas of catalysis, with recent applications to homogeneous catalysts having been pioneered by our research group. Both volcano plots and activity maps are built from linear free energy scaling relationships that connect the value of a descriptor variable(s) with the relative energies of other catalytic cycle intermediates/transition states. These relationships must be both constructed and postprocessed appropriately to obtain the resulting plots/maps; this process requires careful execution to obtain meaningful results. In this protocol, we provide a step-by-step guide to building volcano plots and activity maps using curated reaction profile data. The reaction profile data are obtained using density functional theory computations to model the catalytic cycle. In addition, we provide volcanic, a Python code that automates the steps of the process following data acquisition. Unlike the computation of individual reaction energy profiles, our tools lead to a holistic view of homogeneous catalyst performance that can be broadly applied for both explanatory and screening purposes.

Mapping Active Site Geometry to Activity in Immobilized Frustrated Lewis Pair Catalysts

The immobilization of molecular catalysts imposes spatial constraints on their active site. We reveal that in bifunctional catalysis such constraints can also be utilized as an appealing handle to boost intrinsic activity through judicious control of the active site geometry. To demonstrate this, we develop a pragmatic approach, based on nonlinear scaling relationships, to map the spatial arrangements of the acid–base components of frustrated Lewis pairs (FLPs) to their performance in the catalytic hydrogenation of CO₂. The resulting activity map shows that fixing the donor–acceptor centers at specific distances and locking them into appropriate orientations leads to an unforeseen many‐fold increase in the catalytic activity of FLPs compared to their unconstrained counterparts.

Manpixiao Decoction Halted the Malignant Transformation of Precancerous Lesions of Gastric Cancer: From Network Prediction to In-Vivo Verification

Manpixiao decoction (MPX), a traditional Chinese medicine formula, is mainly used to improve the gastric mucosal pathology and stomach discomfort in patients with gastric precancerous lesions. Precancerous lesion of gastric cancer (PLGC) refers to intestinal metaplasia and/or dysplasia based on gastric mucosal atrophy. Effective prevention and treatment of PLGC is of great significance to reduce the incidence of gastric cancer. Because of the complexity of the etiology and pathogenesis of PLGC, there is no unified and effective treatment plan in western medicine. In recent years, traditional Chinese medicine has shown obvious advantages in the treatment of PLGC and the prevention of its further progression to gastric cancer, relying on its multi-approach and multi-target comprehensive intervention characteristics. This study is designed to examine the protective effect of MPX against PLGC and further to reveal the engaged mechanism via integrating network pharmacology and in vivo experimental evidence. Network pharmacology results demonstrated that inflammation, immune responses, and angiogenesis might be associated with the efficacy of MPX in the treatment of PLGC, in which the PI3K-Akt, cellular senescence, P53 and protein processing in endoplasmic reticulum were involved. Then, we established a rat model of PLGC using a combination of N-methyl-N′-nitro-N-nitrosoguanidine (MNNG), sodium salicylate, irregular fasting, and ranitidine, and observed the effects after MPX treatment. Our result showed that MPX improved the pathological condition of gastric mucosa in PLGC rats and reduced the incidence of gastric cancer. Next, the analysis of serum inflammatory cytokines showed that MPX reduced the inflammation-related cytokines (such as IL-1α, IL-7, CSF-1, and CSF-3) in the serum. Additionally, MPX also had a regulation effect on the “protein/protein phosphorylation-signaling pathway” network in the core region of the PLGC rats. It is showed that MPX can inhibit the phosphorylation of PI3K-AKT, and downregulates the EGFR, β-catenin, and N-cadherin protein levels. These results indicate that MPX halted the PLGC progression through inhibiting EGFR-PI3K-AKT related epithelial-mesenchymal transition process.

Computational Analysis of Structure - Activity Relationships in Highly Active Homogeneous Ruthenium-based Water Oxidation Catalysts

Linear free energy scaling relationships (LFESRs) and regression analysis may predict the catalytic performance of heterogeneous and recently, homogenous water oxidation catalysts (WOCs). This study analyses twelve homogeneous Ru-based catalysts - some, the most active catalysts studied: the Ru(tpy-R)(QC) and Ru(tpy-R)(4-pic)2 catalysts, where tpy is 2,2:6,2-terpyridine, QC is 8-quinolinecarboxylate and 4-pic is 4-picoline. Typical relationships studied among heterogenous and solid-state catalysts cannot be broadly applied to homogeneous catalysts. This subset of structurally similar catalysts with impressive catalytic activity deserves closer computational and statistical analysis of energetics correlating with measured catalytic activity. We report general methods of LFESR analysis yield insufficiently robust relationships between descriptor variables. However, volcano plot-based analysis grounded in Sabatier's principle reveals ranges of ideal relative energies of the RuIV=O and RuIV-OH intermediates and optimal changes in free energies of water nucleophilic attack on RuV=O. A narrow range of RuIV-OH to RuV=O redox potentials corresponding with the highest catalytic activities suggests facile access to the catalytically competent high-valent RuV=O state, often inaccessible from RuIV=O. Our work introduces experimental oxygen evolution rates into approaches of LFESR and Sabatier principle-based analysis, identifying a narrow yet fertile energetic landscape to bountiful oxygen-evolution activity, leading future rational design.

Mapping Catalyst–Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions

Group 9 metals, in particular Rh^III complexes with cyclopentadienyl ligands, are competent C−H activation catalysts. Recently, a Cp*Rh^III‐catalyzed reaction of alkenes with N‐enoxyphthalimides showed divergent outcome based on the solvent, with carboamination favored in methanol and cyclopropanation in 2,2,2‐trifluoroethanol (TFE). Here, we create selectivity and activity maps capable of unravelling the catalyst‐solvent interplay on the outcome of these competing reactions by analyzing 42 cyclopentadienyl metal catalysts, Cp^XM^III (M=Co, Rh, Ir). These maps not only can be used to rationalize previously reported experimental results, but also capably predict the behavior of untested catalyst/solvent combinations as well as aid in identifying experimental protocols that simultaneously optimize both catalytic activity and selectivity (solutions in the Pareto front). In this regard, we demonstrate how and why the experimentally employed Cp*Rh^III catalyst represents an ideal choice to invoke a solvent‐induced change in reactivity. Additionally, the maps reveal the degree to which even perceived minor changes in the solvent (e. g., replacing methanol with ethanol) influence the ratio of carboamination and cyclopropanation products. Overall, the selectivity and activity maps presented here provide a generalizable tool to create global pictures of anticipated reaction outcome that can be used to develop new experimental protocols spanning metal, ligand, and solvent space.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.

The integration of bio-catalysis and electrocatalysis to produce fuels and chemicals from carbon dioxide

The dependence on fossil fuels has caused excessive emissions of greenhouse gases (GHGs), leading to climate changes and global warming. Even though the expansion of electricity generation will enable a wider use of electric vehicles, biotechnology represents an attractive route for producing high-density liquid transportation fuels that can reduce GHG emissions from jets, long-haul trucks and ships. Furthermore, to achieve immediate alleviation of the current environmental situation, besides reducing carbon footprint it is urgent to develop technologies that transform atmospheric CO₂ into fossil fuel replacements. The integration of bio-catalysis and electrocatalysis (bio-electrocatalysis) provides such a promising avenue to convert CO₂ into fuels and chemicals with high-chain lengths. Following an overview of different mechanisms that can be used for CO₂ fixation, we will discuss crucial factors for electrocatalysis with a special highlight on the improvement of electron-transfer kinetics, multi-dimensional electrocatalysts and their hybrids, electrolyser configurations, and the integration of electrocatalysis and bio-catalysis. Finally, we prospect key advantages and challenges of bio-electrocatalysis, and end with a discussion of future research directions.

N-Oxides amplify catalyst reactivity and isoselectivity in the ring-opening polymerization of rac-β-butyrolactone

N-Oxides can amplify the performance of a lanthanum aminobisphenolate catalyst in the ring-opening polymerization (ROP) of rac-β-butyrolactone (rac-BBL) to unprecedented levels (TOF/P_m; At RT: 1900 h^-1/0.73, At -30 °C: 200 h^-1/0.82). Experiments and computations establish donor electronics control catalyst activity, while donor sterics control catalyst deactivation.

Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation

While computational screening with first-principles density functional theory (DFT) is essential for evaluating candidate catalysts, limitations in accuracy typically prevent the prediction of experimentally relevant activities. Exemplary of these challenges are homogeneous water oxidation catalysts (WOCs) where differences in experimental conditions or small changes in ligand structure can alter rate constants by over an order of magnitude. Here, we compute mechanistically relevant electronic and energetic properties for 19 mononuclear Ru transition-metal complexes (TMCs) from three experimental water oxidation catalysis studies. We discover that 15 of these TMCs have experimental activities that correlate with a single property, the ionization potential of the Ru(II)-O₂ catalytic intermediate. This scaling parameter allows the quantitative understanding of activity trends and provides insight into the rate-limiting behavior. We use this approach to rationalize differences in activity with different experimental conditions, and we qualitatively analyze the source of distinct behavior for different electronic states in the other four catalysts. Comparison to closely related single-atom catalysts and modified WOCs enables rationalization of the source of rate enhancement in these WOCs.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity

Enhancing the activity of bifunctional organocatalysts: a fragment-based approach coupled with activity maps helps identifying better-performing catalytic motifs.

The Evolution of Data-Driven Modeling in Organic Chemistry

Organic chemistry is replete with complex relationships: for example, how a reactant's structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst's structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure-activity and structure-property relationships.

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

Novel niobium-doped titanium oxide towards electrochemical destruction of forever chemicals

Electrochemical advanced oxidative processes (EAOP) are a promising route to destroy recalcitrant organic contaminants such as per- and polyfluoroalkyl substances (PFAS) in drinking water. Central to EAOP are catalysis-induced reactive free radicals for breaking the carbon fluorine bonds in PFAS. Generating these reactive species electrochemically at electrodes provides an advantage over other oxidation processes that rely on chemicals or other harsh conditions. Herein, we report on the performance of niobium (Nb) doped rutile titanium oxide (TiO2) as a novel EAOP catalytic material, combining theoretical modeling with experimental synthesis and characterization. Calculations based on density functional theory are used to predict the overpotential for oxygen evolution at these candidate electrodes, which must be high in order to oxidize PFAS. The results indicate a non-monotonic trend in which Nb doping below 6.25 at.% is expected to reduce performance relative to TiO2, while higher concentrations up to 12.5 at.% lead to increased performance, approaching that of state-of-the-art Magnéli Ti4O7. TiO2 samples were synthesized with Nb doping concentration at 10 at.%, heat treated at temperatures from 800 to 1100 °C, and found to exhibit high oxidative stability and high generation of reactive oxygen free radical species. The capability of Nb-doped TiO2 to destroy two common species of PFAS in challenge water was tested, and moderate reduction by ~ 30% was observed, comparable to that of Ti4O7 using a simple three-electrode configuration. We conclude that Nb-doped TiO2 is a promising alternative EAOP catalytic material with increased activity towards generating reactive oxygen species and warrants further development for electrochemically destroying PFAS contaminants.

Uncovering the Activity of Alkaline Earth Metal Hydrogenation Catalysis Through Molecular Volcano Plots

Recent advances in alkaline earth (Ae) metal hydrogenation catalysis have broadened the spectrum of potential catalysts to include candidates from the main group, providing a sustainable alternative to the commonly used transition metals. Although Ae-amides have already been demonstrated to catalyze hydrogenation of imines and alkenes, a lucid understanding of how different metal/ligand combinations influence the catalytic activity is yet to be established. In this article, we use linear scaling relationships and molecular volcano plots to assess the potential of the Ae metal-based catalysts for the hydrogenation of alkenes. By analyzing combinations of eight metals (mono-, bi-, tri-, and tetravalent) and seven ligands, we delineate the impact of metal-ligand interplay on the hydrogenation activity. Our findings highlight that the catalytic activity is majorly determined by the charge and the size of the metal ions. While bivalent Ae metal cations delicately regulate the binding and the release of the reactants and the products, respectively, providing the right balance for this reaction, ligands play only a minor role in determining their catalytic activity. We show how volcano plots can be utilized for the rapid screening of prospective Ae catalysts to establish a guideline to achieve maximum activity in facilitating the hydrogenation process.

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.