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RESEARCH PAPER · ACS Catalysis · 2020

Automated in Silico Design of Homogeneous Catalysts

Marco Foscato, Vidar Remi Jensen

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Automated in Silico Design of Homogeneous CatalystsOriginalLarge-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics41Metal-based electrocatalytic conversion of CO 2 to formic acid/formate211Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions80An Integrated High Throughput Experimentation/Predictive QSAR Modeling Approach to ansa-Zirconocene Catalysts for Isotactic Polypropylene49Theoretical Design of Photofunctional Molecular Aggregates for Optical Properties: An Inverse Design Approach9Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review28cgbind : A Python Module and Web App for Automated Metallocage Construction and Host–Guest Characterization46Enhanced Ab Initio Molecular Dynamics Exploration Unveils the Complex Role of Different Intramolecular Bases on the Water Nucleophilic Attack Mechanism26Design, Molecular Docking And In Silico Analysis Of Analogues Of Chloroquine And Hydroxychloroquine Against SARs-COV-2 Target (6w63.pdb)0Spotting trends in organocatalysis for the next decade54Established and Emerging Computational Tools to Study Homogeneous Catalysis—From Quantum Mechanics to Machine Learning70autodE: Automated Calculation of Reaction Energy Profiles – Application to Organic and Organometallic Reactions1Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening58The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes11A public-private partnership for the express development of antiviral leads: a perspective view6autodE: Automated Calculation of Reaction Energy Profiles— Application to Organic and Organometallic Reactions122autodE: Automated Calculation of Reaction Energy Profiles— Application to Organic and Organometallic Reactions32Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies33Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis39tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138QChASM : Quantum chemistry automation and structure manipulation86Beyond hydrogen bonding: recent trends of outer sphere interactions in transition metal catalysis104Computational insights into metal-catalyzed asymmetric hydrogenation11In silico characterization and prediction of thiourea-like neutral bidentate halogen bond catalysts2Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Neutral and cationic methallyl nickel complexes in alkene activation: a combined DFT, ESI-MS and chemometric approach8Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105The ortho effect in directed C–H activation49Trends in computational molecular catalyst design27Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis76Selectivity in organocatalysis—From qualitative to quantitative predictive models40Computer-assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands10Enantioselective reduction of prochiral ketones promoted by amino amide ruthenium complexes: A DFT study1Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts56A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation54Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products77A Conformer‐Dependent, Quantitative Quadrant Model14Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55Data-Driven Catalyst Optimization for Stereodivergent Asymmetric Synthesis of α-Allyl Carboxylic Acids by Iridium/boron Hybrid Catalysis0The Impact of Computational Uncertainties on the Enantioselectivity Predictions: A Microkinetic Modeling of Ketone Transfer Hydrogenation with a Noyori‐type Mn‐diamine Catalyst10Data-driven catalyst optimization for stereodivergent asymmetric synthesis of α-allyl carboxylic acids by iridium/boron hybrid catalysis0Computational Organometallic Catalysis: Where We Are, Where We Are Going35CAT-COSMO-CAMPD: Integrated in silico design of catalysts and processes based on quantum chemistry11Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Bimolecular Coupling in Olefin Metathesis: Correlating Structure and Decomposition for Leading and Emerging Ruthenium−Carbene Catalysts58The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts22Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States26Mechanisms, challenges, and opportunities of dual Ni/ photoredox‐catalyzed C(sp 2 )–C(sp 3 ) cross‐couplings37Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning9A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis2Applying Active Learning to the Screening of Molecular Oxygen Evolution Catalysts9Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge35Accelerated dinuclear palladium catalyst identification through unsupervised machine learning164Assessing and rationalizing the performance of Hessian update schemes for reaction path Hamiltonian rate calculations2Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis53A transfer learning protocol for chemical catalysis using a recurrent neural network adapted from natural language processing37BatteryDataExtractor: battery-aware text-mining software embedded with BERT models43Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost24Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity14Inverse design of molecule–metal nanoparticle systems interacting with light for desired photophysical properties3Molecular field analysis for data-driven molecular design in asymmetric catalysis14OSCAR: an extensive repository of chemically and functionally diverse organocatalysts39The (not so) simple prediction of enantioselectivity – a pipeline for high-fidelity computations28Unlocking the computational design of metal–organic cages66A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis309Molecular Field Analysis Using Computational-Screening Data in Asymmetric N -Heterocyclic Carbene-Copper Catalysis toward Data-Driven In Silico Catalyst Optimization20Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65Representations and strategies for transferable machine learning improve model performance in chemical discovery19Application and prospect of machine learning in polyolefin catalysts11Genetic Optimization of Homogeneous Catalysts47Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis24Catalyst design within asymmetric organocatalysis20Chemical Bonding in Homogenous Catalysis – Seen Through the Eyes of Vibrational Spectroscopy3Text Mining for Energy Materials3Organic photoredox catalysts for CO2 reduction: Driving discovery with genetic algorithms10Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants41Sharing is Caring: Guidelines for Sharing in the Electronic Laboratory Notebook (ELN) Chemotion as applied by a Synthesis‐oriented Working Group**9Computational Aspects of Single-Molecule Kinetics for Coupled Catalytic Cycles: A Spectral Analysis4Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands18Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation45Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems3OSCAR: An Extensive Repository of Chemically and Functionally Diverse Organocatalysts1Recent advances in catalytic synthesis of pyridine derivatives12Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities50Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe8Me12– and Related Clusters: A Computational Study6Ligand additivity relationships enable efficient exploration of transition metal chemical space22Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37Three Sides of the Same Coin: Combining Microbial, Enzymatic, and Organometallic Catalysis for Integrated Conversion of Renewable Carbon Sources7Towards predictive computational catalysis – a case study of olefin metathesis with Mo imido alkylidene N-heterocyclic carbene catalysts4Deep learning metal complex properties with natural quantum graphs59Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions50High-throughput computational workflow for ligand discovery in catalysis with the CSD9Highly chemoselective ligands for Suzuki–Miyaura cross-coupling reaction based on virtual ligand-assisted screening15Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces53Predicting ruthenium catalysed hydrogenation of esters using machine learning10Theory-guided development of homogeneous catalysts for the reduction of CO 2 to formate, formaldehyde, and methanol derivatives24Computational Approaches in Some Important Organometallic Catalysis Reaction4Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**40Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**13Genetic Algorithms for the Discovery of Homogeneous Catalysts14Interpretable Machine Learning Model for Predicting Interaction Energies between Dimethyl Sulfide and Potential Absorbing Solvents8Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design27A Functionalized Heterogeneous Catalyst from Atomically Precise Pd 1 Au 8 Clusters Facilitates Carbon–Carbon Bond Construction37Updates on Drug Designing Approach Through Computational Strategies: a Review41Stable tetranuclear copper cage as efficient heterogeneous catalyst for C−C and C−N bond forming reactions4Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes21Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces1Machine Learning Prediction of High-Yield Cobalt- and Nickel-Catalyzed Borylations10High‐throughput screening of spin states for transition metal complexes with spin‐polarized extended tight‐binding methods27Active Site Engineering via Optimizing the Heterogeneous Support Structure for Single-Atom Catalysis2ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design2In-Silico-Assisted Derivatization of Triarylboranes for the Catalytic Reductive Functionalization of Amino Acids with H21Rate-enhancing PETase mutations determined through DFT/MM molecular dynamics simulations14ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design21% V Bur index and steric maps: from predictive catalysis to machine learning126Deep Graph Learning for Molecules and Materials3Augmenting genetic algorithms with machine learning for inverse molecular design35OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion19Penta- versus hexa-coordinated iridium catalysts control the reactivity of the direct reductive amination between aliphatic amines and aliphatic ketones: a DFT-guided mechanism0Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts26A stable 3D Sr-MOF as an efficient heterogeneous catalyst for the cycloadditions of CO2 and knoevenagel condensation reactions20Automated de Novo Design of Olefin Metathesis Catalysts: Computational and Experimental Analysis of a Simple Thermodynamic Design Criterion13Paving the road towards automated homogeneous catalyst design32On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set2Computational Discovery of Codoped Single-Atom Catalysts for Methane-to-Methanol Conversion11MACE: Automated Assessment of Stereochemistry of Transition Metal Complexes and Its Applications in Computational Catalysis18Transferring hydroformylation reaction into high-pressure gas–liquid microfluidic systems: Key achievements and perspectives9Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction18Directional multiobjective optimization of metal complexes at the billion-system scale42In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H223α-Zr(HPO4)2·H2O: As an effective and recoverable catalyst for the synthesis of a family of quinoxalines in semi-aqueous conditions9Terms of Latin origin relating to sample characterization (IUPAC Technical Report)4Quantum chemical package Jaguar: A survey of recent developments and unique features62Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts14Machine learning-based design of pincer catalysts for polymerization reaction7Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation31Inverse Design of Singlet‐Fission Materials with Uncertainty‐Controlled Genetic Optimization7Inverse Design of Singlet‐Fission Materials with Uncertainty‐Controlled Genetic Optimization15Concatenating Microbial, Enzymatic, and Organometallic Catalysis for Integrated Conversion of Renewable Carbon Sources20Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis4Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery6Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction14Homogenous amination in bio-based chemistry1

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