BetaCitation Atlas

RESEARCH PAPER · Chemical Science · 2020

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

Pascal Friederich, Gabe Gomes, Riccardo De Bin, Alán Aspuru‐Guzik, David Balcells

Read original paper ↗

See where this research leads.

177 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complexOriginalQuantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization81Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces1Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis92The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes11Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies204tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138Computational Methods for Training Set Selection and Error Assessment Applied to Catalyst Design: Guidelines for Deciding Which Reactions to Run First and Which to Run Next0Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts51Predicting glycosylation stereoselectivity using machine learning62Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next25Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts105Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases62Active learning accelerates ab initio molecular dynamics on reactive energy surfaces93Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning88Data-Driven Strategies for Accelerated Materials Design473Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties153TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes0Organic reactivity from mechanism to machine learning175Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts56What's Left for a Computational Chemist To Do in the Age of Machine Learning?10A Conformer‐Dependent, Quantitative Quadrant Model14Metal‐ligand cooperative activation of HX (X=H, Br, OR) bond on Mn based pincer complexes13Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold1ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold0ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold1Computational Organometallic Catalysis: Where We Are, Where We Are Going35Specification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contribute5Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space69Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density38Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States26Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning9Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands6Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge35Understanding the Binding Properties of N‐heterocyclic Carbenes through BDE Matrix App8ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold0Seeking the Optimal Descriptor for S N 2 Reactions through Statistical Analysis of Density Functional Theory Results12Machine learning activation energies of chemical reactions83A predictive journey towards trans -thioamides/amides14ChemSpaX : exploration of chemical space by automated functionalization of molecular scaffold15Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions31Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening30Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining50Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction42A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis309Representations and strategies for transferable machine learning improve model performance in chemical discovery19From the Felkin‐Anh Rule to the Grignard Reaction: an Almost Circular 50 Year Adventure in the World of Molecular Structures and Reaction Mechanisms with Computational Chemistry**2Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability93Machine learning studies on asymmetric relay Heck reaction—Potential avenues for reaction development20Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study9Machine intelligence for chemical reaction space124Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis24Machine Learning for the Discovery, Design, and Engineering of Materials66Deep dive into machine learning density functional theory for materials science and chemistry111Data‐Driven Materials Innovation and Applications187New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts74Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications1Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling57Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr18Physics-based representations for machine learning properties of chemical reactions33A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System23Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe8Me12– and Related Clusters: A Computational Study6Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors18Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model31Data-driven ligand field exploration of Fe( iv )–oxo sites for C–H activation16Deep learning metal complex properties with natural quantum graphs59Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions50Low-cost machine learning prediction of excited state properties of iridium-centered phosphors40Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach24Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces53Predicting ruthenium catalysed hydrogenation of esters using machine learning10Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights24Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions50Indolizine Synthesis through Annulation of Pyridinium 1,4‐Thiolates and Copper Carbenes: A Predictive Catalysis Approach41Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations28The Role of AI in Driving the Sustainability of the Chemical Industry16Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design27Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge73Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces1Predicting electronic structures at any length scale with machine learning70Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis43Correlation between the Experimental and Theoretical Photoelectrochemical Response of a WO3 Electrode for Efficient Water Splitting through the Implementation of an Artificial Neural Network14Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery19ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design2AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials0Evolutionary Algorithms and Workflows for De Novo Catalyst Design1HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts15Prediction of Reaction Orthogonality using Machine Learning1Discovery of Hemilabile Ligands Using MachineLearning1Reformulating Reactivity Design for Data-Efficient Machine Learning14The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory13Predictive catalysis: a valuable step towards machine learning47ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design21Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling8Classification of Hemilabile Ligands Using Machine Learning15Estimation of the Surface Free Energy Components for Solid Surfaces: A Machine Learning Approach43DReact: Geometric Deep Learning for Chemical Reactions3% V Bur index and steric maps: from predictive catalysis to machine learning126Deep Graph Learning for Molecules and Materials3Benchmarking machine-readable vectors of chemical reactions on computed activation barriers9Every atom counts: predicting sites of reaction based on chemistry within two bonds0Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity11Molecular modelling of encapsulation and reactivity within metal-organic cages (MOCs)1OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion19Tailoring phosphine ligands for improved C–H activation: insights from Δ-machine learning0Toward AI/ML-assisted discovery of transition metal complexes4Tuning the steric hindrance of alkylamines: a predictive model of steric editing of planar amines15Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning27Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle8Machine Learning Models for Predicting Zirconocene Properties and Barriers5Paving the road towards automated homogeneous catalyst design32On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set2Unveiling similarities and differences in oxidation processes of oxidants and derived reactive oxygen species through machine learning interpretation of oxidation rate constants32Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability22Directional multiobjective optimization of metal complexes at the billion-system scale42Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields2Recent computational insights into hydrogen storage by MXene-based materials and shedding light on the storage mechanism373DReact: Geometric Deep Learning for Chemical Reactions24Integrating digital chemistry within the broader chemistry community11Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks11ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Machine-learning models for atom-diatom reactions across isotopologues3Reinforcement Learning for Improving Chemical Reaction Performance15Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis4AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning11Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport12Computational tools for the prediction of site- and regioselectivity of organic reactions33Predicting hydrogen atom transfer energy barriers using Gaussian process regression5Screening Diels–Alder reaction space to identify candidate reactions for self-healing polymer applications9Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes3Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides5Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis9High-Throughput Optimization of a High-Pressure Catalytic Reaction5Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Applications of density functional theory and machine learning in nanomaterials: A review51Machine Learning Transition State Geometries and Applications in Reaction Property Prediction9Machine‐Learning‐Based Design of Metallocene Catalysts for Controlled Olefin Copolymerization5AI Approaches to Homogeneous Catalysis with Transition Metal Complexes47Improving generative inverse design of molecular catalysts in small data regime3Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships1Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means0Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics0Predicting Reactivity with Machine Learning1Building hybrid AI models in chemical engineering: A tutorial review17Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models23Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations1

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

177 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

    Chemical Science · 2020-01-01

    Steven M. Maley, Doo‐Hyun Kwon, Nick Rollins, Johnathan C. Stanley, Orson L. Sydora, Steven M. Bischof, Daniel H. Ess

    Read original paper ↗ ·

  2. Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

    ChemRxiv · 2020-02-28

    Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli

    Read original paper ↗ ·

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

    Journal of the American Chemical Society · 2020-06-22

    Jeremy J. Henle, Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Yang Wang, Scott E. Denmark

    Read original paper ↗ ·

  4. The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes

    ChemRxiv · 2020-08-31

    David Balcells, Bastian Bjerkem Skjelstad

    Read original paper ↗ ·

  5. Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

    Chemical Science · 2020-11-05

    Kjell Jorner, Tore Brinck, Per‐Ola Norrby, David Buttar

    Read original paper ↗ ·

  6. tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes

    Journal of Chemical Information and Modeling · 2020-11-09

    David Balcells, Bastian Bjerkem Skjelstad

    Read original paper ↗ ·

  7. 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

    ChemRxiv · 2020-11-16

    Scott E. Denmark, Andrew F. Zahrt, William T. Darrow, Brennan T. Rose, Jeremy J. Henle

    Read original paper ↗ ·

  8. Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

    ACS Catalysis · 2020-12-07

    Aditya Nandy, Heather J. Kulik

    Read original paper ↗ ·

  9. Predicting glycosylation stereoselectivity using machine learning

    Chemical Science · 2020-12-26

    Sooyeon Moon, Sourav Chatterjee, Peter H. Seeberger, Kerry M. Gilmore

    Read original paper ↗ ·

  10. 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

    Reaction Chemistry & Engineering · 2021-01-01

    Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Jeremy J. Henle, Scott E. Denmark

    Read original paper ↗ ·

  11. Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

    Chemical Science · 2021-01-01

    Simone Gallarati, Raimón Fabregat, Rubén Laplaza, Sinjini Bhattacharjee, Matthew D. Wodrich, Clémence Corminbœuf

    Read original paper ↗ ·

  12. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-01-05

    José Peña Guerrero, Paul Nguewa, Alfonso T. García‐Sosa

    Read original paper ↗ ·

  13. Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

    Chem · 2021-01-10

    Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli

    Read original paper ↗ ·

  14. Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

    Trends in Chemistry · 2021-01-14

    Gabe Gomes, Robert Pollice, Alán Aspuru‐Guzik

    Read original paper ↗ ·

  15. Data-Driven Strategies for Accelerated Materials Design

    Accounts of Chemical Research · 2021-02-02

    Robert Pollice, Gabe Gomes, Matteo Aldeghi, Riley J. Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao, Alán Aspuru‐Guzik

    Read original paper ↗ ·

  16. Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

    Accounts of Chemical Research · 2021-02-03

    Liliana C. Gallegos, Guilian Luchini, Peter C. St. John, Seonah Kim, Robert S. Paton

    Read original paper ↗ ·

  17. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

    ChemRxiv · 2021-02-24

    Filipe Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro

    Read original paper ↗ ·

  18. TMtopo Dataset — Quantum Geometries and Density Topology for 1.1k Transition Metal Complexes

    ChemRxiv · 2021-02-25

    Filipe B. Teixeira, Edgar Silva-Santos, M. Natália D. S. Cordeiro

    Read original paper ↗ ·

  19. Organic reactivity from mechanism to machine learning

    Nature Reviews Chemistry · 2021-03-16

    Kjell Jorner, Anna Tomberg, Christoph Alexander Bauer, Christian Sköld, Per‐Ola Norrby

    Read original paper ↗ ·

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

    Accounts of Chemical Research · 2021-04-15

    N. Ian Rinehart, Andrew F. Zahrt, Jeremy J. Henle, Scott E. Denmark

    Read original paper ↗ ·

  21. What's Left for a Computational Chemist To Do in the Age of Machine Learning?

    Israel Journal of Chemistry · 2021-04-15

    Heather J. Kulik

    Read original paper ↗ ·

  22. A Conformer‐Dependent, Quantitative Quadrant Model

    European Journal of Organic Chemistry · 2021-04-21

    Andrew F. Zahrt, N. Ian Rinehart, Scott E. Denmark

    Read original paper ↗ ·

  23. Metal‐ligand cooperative activation of HX (X=H, Br, OR) bond on Mn based pincer complexes

    Zeitschrift für anorganische und allgemeine Chemie · 2021-05-11

    Annika M. Krieger, Vivek Sinha, Adarsh V. Kalikadien, Evgeny A. Pidko

    Read original paper ↗ ·

  24. Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

    The Journal of Physical Chemistry Letters · 2021-05-11

    Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik

    Read original paper ↗ ·

  25. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

    ChemRxiv · 2021-05-21

    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha

    Read original paper ↗ ·

  26. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

    ChemRxiv · 2021-05-21

    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha

    Read original paper ↗ ·

  27. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

    ChemRxiv · 2021-06-08

    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha

    Read original paper ↗ ·

  28. Computational Organometallic Catalysis: Where We Are, Where We Are Going

    European Journal of Inorganic Chemistry · 2021-06-17

    Agustı́ Lledós

    Read original paper ↗ ·

  29. Specification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contribute

    Structural Equation Modeling A Multidisciplinary Journal · 2021-06-24

    Bjørn Gunnar Hansen, Ulf Henning Olsson

    Read original paper ↗ ·

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

    Chemical Reviews · 2021-07-14

    Aditya Nandy, Chenru Duan, Michael G. Taylor, Fang Liu, Adam H. Steeves, Heather J. Kulik

    Read original paper ↗ ·

  31. Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

    The Journal of Chemical Physics · 2021-08-10

    Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld

    Read original paper ↗ ·

  32. Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density

    Journal of Chemical Theory and Computation · 2021-09-03

    Santiago Vargas, Matthew R. Hennefarth, Z. J. Liu, Anastassia N. Alexandrova

    Read original paper ↗ ·

  33. Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States

    Topics in Catalysis · 2021-09-12

    Shusen Chen, Taylor Nielson, Elayna Zalit, Bastian Bjerkem Skjelstad, Braden Borough, William J. Hirschi, Spencer Yu, David Balcells, Daniel H. Ess

    Read original paper ↗ ·

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

    The Journal of Physical Chemistry Letters · 2021-10-01

    Michael G. Taylor, Aditya Nandy, Connie C. Lu, Heather J. Kulik

    Read original paper ↗ ·

  35. Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

    The Journal of Physical Chemistry B · 2021-10-25

    Lu Han, Xiaohui Kang, Yi Luo

    Read original paper ↗ ·

  36. Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-11-23

    Natalie Fey, Jason M. Lynam

    Read original paper ↗ ·

  37. Understanding the Binding Properties of N‐heterocyclic Carbenes through BDE Matrix App

    European Journal of Inorganic Chemistry · 2021-11-29

    Lucía Morán‐González, Jaime Rodrı́guez-Guerra, María Besora, Feliu Maseras

    Read original paper ↗ ·

  38. ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

    ChemRxiv · 2021-12-01

    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha

    Read original paper ↗ ·

  39. Seeking the Optimal Descriptor for S N 2 Reactions through Statistical Analysis of Density Functional Theory Results

    The Journal of Organic Chemistry · 2021-12-22

    Lucía Morán‐González, María Besora, Feliu Maseras

    Read original paper ↗ ·

  40. Machine learning activation energies of chemical reactions

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2021-12-30

    Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson

    Read original paper ↗ ·

  41. A predictive journey towards trans -thioamides/amides

    Chemical Communications · 2022-01-01

    Michele Tomasini, Jin Zhang, Hui Chuan Zhao, Emili Besalú, Laura Falivene, Lucia Caporaso, Michal Szostak, Albert Poater

    Read original paper ↗ ·

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

    Digital Discovery · 2022-01-01

    Adarsh V. Kalikadien, Evgeny A. Pidko, Vivek Sinha

    Read original paper ↗ ·

  43. Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions

    Digital Discovery · 2022-01-01

    Ajnabiul Hoque, Raghavan B. Sunoj

    Read original paper ↗ ·

  44. Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening

    Journal of Materials Chemistry A · 2022-01-01

    Cheng-Han Li, Daniel P. Tabor

    Read original paper ↗ ·

  45. Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

    Chemical Science · 2022-01-01

    Mingjian Wen, Samuel M. Blau, Xiaowei Xie, Shyam Dwaraknath, Kristin Aslaug Persson

    Read original paper ↗ ·

  46. Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction

    Chemical Science · 2022-01-01

    Elliot H. E. Farrar, Matthew N. Grayson

    Read original paper ↗ ·

  47. A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

    Journal of the American Chemical Society · 2022-01-12

    Tobias Gensch, Gabe Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice, Kjell Jorner, AkshatKumar Nigam, Michael Lindner-D’Addario, Matthew S. Sigman, Alán Aspuru‐Guzik

    Read original paper ↗ ·

  48. Representations and strategies for transferable machine learning improve model performance in chemical discovery

    The Journal of Chemical Physics · 2022-01-27

    Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik

    Read original paper ↗ ·

  49. From the Felkin‐Anh Rule to the Grignard Reaction: an Almost Circular 50 Year Adventure in the World of Molecular Structures and Reaction Mechanisms with Computational Chemistry**

    Israel Journal of Chemistry · 2022-02-01

    Odile Eisenstein

    Read original paper ↗ ·

  50. Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability

    The Journal of Chemical Physics · 2022-02-22

    Thijs Stuyver, Connor W. Coley

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.