Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

The application of modern machine learning to challenges in atomistic simulation is gaining attraction.

[1]  Jaehoon Kim,et al.  Active learning with non-ab initio input features toward efficient CO2 reduction catalysts , 2018, Chemical science.

[2]  O. Anatole von Lilienfeld,et al.  Quantum Machine Learning in Chemical Compound Space , 2018 .

[3]  Matthew D. Wodrich,et al.  On the Generality of Molecular Volcano Plots , 2018 .

[4]  Matthew D. Wodrich,et al.  Improving the Thermodynamic Profiles of Prospective Suzuki–Miyaura Cross‐Coupling Catalysts by Altering the Electrophilic Coupling Component , 2018 .

[5]  John R. Kitchin,et al.  Machine learning in catalysis , 2018, Nature Catalysis.

[6]  A. Rappe,et al.  Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning. , 2018, Journal of the American Chemical Society.

[7]  Heather J Kulik,et al.  Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. , 2018, The journal of physical chemistry letters.

[8]  Anders S. Christensen,et al.  Alchemical and structural distribution based representation for universal quantum machine learning. , 2017, The Journal of chemical physics.

[9]  Luke E K Achenie,et al.  High-throughput screening of bimetallic catalysts enabled by machine learning , 2017 .

[10]  George E. Dahl,et al.  Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. , 2017, Journal of chemical theory and computation.

[11]  D. Lu,et al.  Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles. , 2017, The journal of physical chemistry letters.

[12]  Nathan S. Lewis,et al.  Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction , 2017 .

[13]  Ryosuke Jinnouchi,et al.  Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm. , 2017, The journal of physical chemistry letters.

[14]  H. Kulik,et al.  Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. , 2017, The journal of physical chemistry. A.

[15]  M. Stamatakis,et al.  A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers. , 2017, The Journal of chemical physics.

[16]  Matthew D. Wodrich,et al.  A Generalized Picture of C-C Cross-Coupling , 2017 .

[17]  M. Szostak,et al.  Pd-PEPPSI: Pd-NHC Precatalyst for Suzuki-Miyaura Cross-Coupling Reactions of Amides. , 2017, The Journal of organic chemistry.

[18]  Noam Bernstein,et al.  Machine learning unifies the modeling of materials and molecules , 2017, Science Advances.

[19]  Matthew S. Sigman,et al.  Developing Comprehensive Computational Parameter Sets To Describe the Performance of Pyridine-Oxazoline and Related Ligands , 2017 .

[20]  Hongbo Shi,et al.  Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models , 2017 .

[21]  Zachary W. Ulissi,et al.  To address surface reaction network complexity using scaling relations machine learning and DFT calculations , 2017, Nature Communications.

[22]  Heather J Kulik,et al.  Predicting electronic structure properties of transition metal complexes with neural networks† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01247k , 2017, Chemical science.

[23]  Colin F. Dickens,et al.  Combining theory and experiment in electrocatalysis: Insights into materials design , 2017, Science.

[24]  E. Thiery,et al.  Emergence of Copper‐Mediated Formation of C–C Bonds , 2017 .

[25]  Raghunathan Ramakrishnan,et al.  Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties. , 2016, The journal of physical chemistry letters.

[26]  Anat Milo,et al.  Developing a Modern Approach To Account for Steric Effects in Hammett-Type Correlations. , 2016, Journal of the American Chemical Society.

[27]  Zachary W. Ulissi,et al.  Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning. , 2016, The journal of physical chemistry letters.

[28]  Zhi-Min Chen,et al.  Palladium-Catalyzed Enantioselective Redox-Relay Heck Arylation of 1,1-Disubstituted Homoallylic Alcohols. , 2016, Journal of the American Chemical Society.

[29]  O. A. von Lilienfeld,et al.  Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity. , 2016, The Journal of chemical physics.

[30]  Matthew D. Wodrich,et al.  Accessing and predicting the kinetic profiles of homogeneous catalysts from volcano plots , 2016, Chemical science.

[31]  Koji Tsuda,et al.  Machine-learning prediction of the d-band center for metals and bimetals , 2016 .

[32]  Anat Milo,et al.  The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond. , 2016, Accounts of chemical research.

[33]  Anat Milo,et al.  Parameterization of phosphine ligands reveals mechanistic pathways and predicts reaction outcomes , 2016, Nature Chemistry.

[34]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[35]  Gadi Rothenberg,et al.  Predicting the performance of oxidation catalysts using descriptor models , 2016 .

[36]  Luke E K Achenie,et al.  Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening. , 2015, The journal of physical chemistry letters.

[37]  T. Sperger,et al.  Computational Studies of Synthetically Relevant Homogeneous Organometallic Catalysis Involving Ni, Pd, Ir, and Rh: An Overview of Commonly Employed DFT Methods and Mechanistic Insights. , 2015, Chemical reviews.

[38]  K. Müller,et al.  Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space , 2015, The journal of physical chemistry letters.

[39]  M. Rupp,et al.  Machine Learning for Quantum Mechanical Properties of Atoms in Molecules , 2015, 1505.00350.

[40]  Philippe Sautet,et al.  Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers. , 2015, Nature chemistry.

[41]  D. Vlachos,et al.  Group Additivity and Modified Linear Scaling Relations for Estimating Surface Thermochemistry on Transition Metal Surfaces: Application to Furanics , 2015 .

[42]  Matthias Rupp,et al.  Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. , 2015, Journal of chemical theory and computation.

[43]  S. K. Gurung,et al.  Copper-catalysed cross-coupling: an untapped potential. , 2015, Organic & biomolecular chemistry.

[44]  Boris Kozinsky,et al.  AiiDA: Automated Interactive Infrastructure and Database for Computational Science , 2015, ArXiv.

[45]  Felix A Faber,et al.  Crystal structure representations for machine learning models of formation energies , 2015, 1503.07406.

[46]  O. A. von Lilienfeld,et al.  Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules. , 2015, Journal of chemical theory and computation.

[47]  Quanrui Wang,et al.  Palladium catalyzed asymmetric Suzuki–Miyaura coupling reactions to axially chiral biaryl compounds: Chiral ligands and recent advances , 2015 .

[48]  H. Rao,et al.  Copper-catalyzed C(sp3)-OH cleavage with concomitant C-C coupling: synthesis of 3-substituted isoindolinones. , 2015, The Journal of organic chemistry.

[49]  Kevin Bateman,et al.  Nanomole-scale high-throughput chemistry for the synthesis of complex molecules , 2015, Science.

[50]  V. Ananikov Understanding Organometallic Reaction Mechanisms and Catalysis: Computational and Experimental Tools , 2014 .

[51]  Frank Glorius,et al.  Contemporary screening approaches to reaction discovery and development. , 2014, Nature chemistry.

[52]  Tom K Woo,et al.  Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture. , 2014, The journal of physical chemistry letters.

[53]  Pavlo O. Dral,et al.  Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.

[54]  T. Jamison,et al.  Recent advances in homogeneous nickel catalysis , 2014, Nature.

[55]  S. Bräse,et al.  Metal-catalyzed cross-coupling reactions and more , 2014 .

[56]  F. Maseras,et al.  Rationale for the sluggish oxidative addition of aryl halides to Au(i) , 2014, Chemical communications.

[57]  G. Rothenberg,et al.  Heterogeneous catalyst discovery using 21st century tools: a tutorial , 2014 .

[58]  S. Bräse,et al.  Metal-catalyzed cross-coupling reactions and more , 2014 .

[59]  P. Sabatier La Catalyse en chimie organique , 2013 .

[60]  P. Chirik,et al.  Cobalt Precursors for High-Throughput Discovery of Base Metal Asymmetric Alkene Hydrogenation Catalysts , 2013, Science.

[61]  Klaus-Robert Müller,et al.  Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.

[62]  Tom K. Woo,et al.  Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity , 2013 .

[63]  M. Rupp,et al.  Machine learning of molecular electronic properties in chemical compound space , 2013, 1305.7074.

[64]  Fu‐She Han Transition-metal-catalyzed Suzuki-Miyaura cross-coupling reactions: a remarkable advance from palladium to nickel catalysts. , 2013, Chemical Society reviews.

[65]  Y. Nishihara Applied Cross-Coupling Reactions , 2013 .

[66]  G. Rothenberg,et al.  New tricks by very old dogs: predicting the catalytic hydrogenation of HMF derivatives using Slater-type orbitals , 2012 .

[67]  Nenad M. Markovic,et al.  The road from animal electricity to green energy: combining experiment and theory in electrocatalysis , 2012 .

[68]  Frank R Burden,et al.  Quantitative structure-property relationship modeling of diverse materials properties. , 2012, Chemical reviews.

[69]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.

[70]  S. D. Walker Cross Coupling and Heck-Type Reactions 1 C — C Cross Coupling Using Organometallic Partners , 2012 .

[71]  H. Xin,et al.  Predictive Structure–Reactivity Models for Rapid Screening of Pt-Based Multimetallic Electrocatalysts for the Oxygen Reduction Reaction , 2012 .

[72]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[73]  M. Sigman,et al.  Three-Dimensional Correlation of Steric and Electronic Free Energy Relationships Guides Asymmetric Propargylation , 2011, Science.

[74]  John F. Hartwig,et al.  A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions , 2011, Science.

[75]  A. Suzuki Cross-coupling reactions of organoboranes: an easy way to construct C-C bonds (Nobel Lecture). , 2011, Angewandte Chemie.

[76]  John Kitchin,et al.  Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces , 2011 .

[77]  Stefan Grimme,et al.  Effect of the damping function in dispersion corrected density functional theory , 2011, J. Comput. Chem..

[78]  S. Shaik,et al.  How to conceptualize catalytic cycles? The energetic span model. , 2011, Accounts of chemical research.

[79]  H. Gerischer Mechanismus der Elektrolytischen Wasserstoffabscheidung und Adsorptionsenergie von Atomarem Wasserstoff , 2010 .

[80]  Christian Limberg,et al.  The Mechanism of Water Oxidation: From Electrolysis via Homogeneous to Biological Catalysis , 2010 .

[81]  Gadi Rothenberg,et al.  Predictive modeling in homogeneous catalysis: a tutorial. , 2010, Chemical Society reviews.

[82]  S. Grimme,et al.  A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. , 2010, The Journal of chemical physics.

[83]  Scaling relationship for oscillating electrochemical systems: dependence of phase diagram on electrode size and rotation rate. , 2009, Physical chemistry chemical physics : PCCP.

[84]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[85]  S. Buchwald,et al.  Palladium-catalyzed Suzuki-Miyaura cross-coupling reactions employing dialkylbiaryl phosphine ligands. , 2008, Accounts of chemical research.

[86]  G. Swiegers Mechanical Catalysis: Methods of Enzymatic, Homogeneous, and Heterogeneous Catalysis , 2008 .

[87]  S. Buchwald,et al.  Biaryl phosphane ligands in palladium-catalyzed amination. , 2008, Angewandte Chemie.

[88]  R. Paciello,et al.  High-throughput and parallel screening methods in asymmetric hydrogenation. , 2006, Chemical reviews.

[89]  M. Hoshi,et al.  Construction of Terminal Conjugated Enynes: Cu-Mediated Cross-Coupling Reaction of Alkenyldialkylborane with (Trimethylsilyl)ethynyl Bromide , 2006 .

[90]  F. Weigend,et al.  Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. , 2005, Physical chemistry chemical physics : PCCP.

[91]  Thomas Bligaard,et al.  Trends in the exchange current for hydrogen evolution , 2005 .

[92]  K. Morokuma,et al.  Theoretical Insight into the C−C Coupling Reactions of the Vinyl, Phenyl, Ethynyl, and Methyl Complexes of Palladium and Platinum , 2005 .

[93]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[94]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[95]  Santosh Putta,et al.  Machine-learning models for combinatorial catalyst discovery , 2003 .

[96]  Samantha L. Hazelwood,et al.  Platinum Catalysts for Suzuki Biaryl Coupling Reactions , 2002 .

[97]  J. Pople,et al.  Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements , 2002 .

[98]  Manfred T Reetz,et al.  New methods for the high-throughput screening of enantioselective catalysts and biocatalysts. , 2002, Angewandte Chemie.

[99]  J. Friedman Stochastic gradient boosting , 2002 .

[100]  M. Reetz Combinatorial and Evolution-Based Methods in the Creation of Enantioselective Catalysts. , 2001, Angewandte Chemie.

[101]  Selim Senkan,et al.  Combinatorial Heterogeneous Catalysis-A New Path in an Old Field. , 2001, Angewandte Chemie.

[102]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[103]  C. Mateo,et al.  INTRAMOLECULAR TRANSMETALATION OF ARYLPALLADIUM(II) AND ARYLPLATINUM(II) COMPLEXES WITH SILANES AND STANNANES , 1998 .

[104]  Eric N. Jacobsen,et al.  SCHIFF BASE CATALYSTS FOR THE ASYMMETRIC STRECKER REACTION IDENTIFIED AND OPTIMIZED FROM PARALLEL SYNTHETIC LIBRARIES , 1998 .

[105]  T. Halgren Merck molecular force field. II. MMFF94 van der Waals and electrostatic parameters for intermolecular interactions , 1996 .

[106]  Thomas A. Halgren,et al.  Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules , 1996, J. Comput. Chem..

[107]  Thomas A. Halgren,et al.  Merck molecular force field. IV. conformational energies and geometries for MMFF94 , 1996, J. Comput. Chem..

[108]  Thomas A. Halgren,et al.  Merck molecular force field. III. Molecular geometries and vibrational frequencies for MMFF94 , 1996, J. Comput. Chem..

[109]  T. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[110]  Norio Miyaura,et al.  Palladium-Catalyzed Cross-Coupling Reactions of Organoboron Compounds , 1995 .

[111]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[112]  M. Frisch,et al.  Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields , 1994 .

[113]  Lawrence D. Jackel,et al.  Learning Curves: Asymptotic Values and Rate of Convergence , 1993, NIPS.

[114]  A. Becke Density-functional thermochemistry. III. The role of exact exchange , 1993 .

[115]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[116]  Parr,et al.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.

[117]  S. Trasatti Electrocatalysis in the anodic evolution of oxygen and chlorine , 1984 .

[118]  J. Bockris,et al.  The Electrocatalysis of Oxygen Evolution on Perovskites , 1984 .

[119]  J. Pople,et al.  Self-consistent molecular orbital methods. 24. Supplemented small split-valence basis sets for second-row elements , 1982 .

[120]  Mark S. Gordon,et al.  Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements , 1980 .

[121]  Norio Miyaura,et al.  A new stereospecific cross-coupling by the palladium-catalyzed reaction of 1-alkenylboranes with 1-alkenyl or 1-alkynyl halides , 1980 .

[122]  K. Nagorny,et al.  Cluster-Bildung in Cu—Fe-Mischkristallen und Bestimmung der Löslichkeit von Eisen in Kupfer im Temperaturbereich von 450—1000°C , 1977 .

[123]  J. Pople,et al.  Self‐Consistent Molecular‐Orbital Methods. IX. An Extended Gaussian‐Type Basis for Molecular‐Orbital Studies of Organic Molecules , 1971 .

[124]  R. Parsons The rate of electrolytic hydrogen evolution and the heat of adsorption of hydrogen , 1958 .

[125]  Edward Teller,et al.  Interaction of the van der Waals Type Between Three Atoms , 1943 .

[126]  L. Hammett,et al.  Linear free energy relationships in rate and equilibrium phenomena , 1938 .

[127]  M. G. Evans,et al.  Inertia and driving force of chemical reactions , 1938 .

[128]  L. Hammett The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives , 1937 .

[129]  R. Bell,et al.  The Theory of Reactions Involving Proton Transfers , 1936 .

[130]  L. Hammett,et al.  Some Relations between Reaction Rates and Equilibrium Constants. , 1935 .