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RESEARCH PAPER · Accounts of Chemical Research · 2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Jon Paul Janet, Chenru Duan, Aditya Nandy, Fang Liu, Heather J. Kulik

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Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles DesignOriginalA framework for constructing linear free energy relationships to design molecular transition metal catalysts13Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55stk : An extendable Python framework for automated molecular and supramolecular structure assembly and discovery58Computational Scaling Relationships Predict Experimental Activity and Rate Limiting Behavior in Homogenous Water Oxidation1Melding of Experiment and Theory Illuminates Mechanisms of Metal-Catalyzed Rearrangements: Computational Approaches and Caveats7Deciphering 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 Ligands6Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost24nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset18OSCAR: an extensive repository of chemically and functionally diverse organocatalysts39Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation9Mechanistically Guided Workflow for Relating Complex Reactive Site Topologies to Catalyst Performance in C–H Functionalization Reactions40Representations and strategies for transferable machine learning improve model performance in chemical discovery19Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design3Data-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 Regimes37The Theoretical Topology and Implementation of Enterprise Social Security in the Digital Age Based on Big Data and Artificial Intelligence3Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow0Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands18OSCAR: An Extensive Repository of Chemically and Functionally Diverse Organocatalysts1A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System23SELFIES and the future of molecular string representations261Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis51Ligand additivity relationships enable efficient exploration of transition metal chemical space22Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Genetic Algorithms for the Discovery of Homogeneous Catalysts14Architector for high-throughput cross-periodic table 3D complex building49Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes21Substituent group tuned structural dimensionalities and magnetic properties of V-shaped bis-pyridyl ligands based Fe(II) coordination polymers5ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design21Maschinelles Lernen zur Vorhersage antibakterieller Aktivität von Ruthenium‐Komplexen**0Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**44Automated prediction of ground state spin for transition metal complexes13Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity11The Metal-Ligand Local Mode as a Descriptor for Catalytic Activity3Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning27Comparison of dimensionality reduction techniques for the visualisation of chemical space in organometallic catalysis17Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals14Automated Prediction of Ground State Spin for Transition Metal Complexes0Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library2Many-body expansion based machine learning models for octahedral transition metal complexes2ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds4Machine learning screening and DFT calculations for exploring Cs2Ag0.75X0.25BiCl6 (X=Na, K, Rb) double perovskites as underwater photovoltaic materials6Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships1Feature-Driven Prediction of HOMO–LUMO Gaps in Transition-Metal Complexes Using the SLEET Model: A SMILES-Based Transformer Framework2Mechanistically guided univariate structure-selectivity relationship in chromium-catalyzed ethylene tetramerization0Machine learning in theoretical study of single-molecule magnets: Current status and challenges0Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence12Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability3Unlabeled but Not Unseen: Cytotoxicity Classification of Re(I) Tricarbonyl Complexes via K-Means Clustering0The BOS-Lig Data Set: Accurate Ligand Charges from a Consensus Approach for 66,810 Experimentally Synthesized Ligands1Bond-Level Prediction of U-Donor Bond Lengths in Uranyl Coordination Complexes Using Machine Learning for Ligand Recognition0

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