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Zeolite encapsulated organometallic complexes as model catalysts

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Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor

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Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?

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A Statistical Modeling Approach to Catalyst Generality Assessment in Enantioselective SynthesisPreprint

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Machine learning enables the prediction of amide bond synthesis based on small datasets

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Computational Study on Data Integration in Chiral Catalyst Design: A Case Study Using Michael Addition Reactions

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Deep learning for asymmetric catalysis

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Chiral Analysis for High‐Throughput Reaction Screening: Recent Advances in Accelerating Asymmetric Catalysis Discovery

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An overview of reaction outcome prediction with physics-based and data-driven methods

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Concise Total Synthesis of (+)-Shearilicine: A Machine Learning-Assisted Strategy for Ligand Optimization of an Enantioselective Palladium-Catalyzed α-Arylation

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AI-Integrated LC–MS/MS Bioanalytical Platforms for Matrix-Effect Mitigation: Advances in Real-Time Monitoring, Sample Preparation, and Validation Challenges

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Development of Catalyst Grading System for Diesel Hydrodesulfurization Using Machine Learning Techniques

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Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors**

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Incorporating Domain Knowledge and Structure-Based Descriptors for Machine Learning: A Case Study of Pd-Catalyzed Sonogashira Reactions

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Computational chemistry of natural product analogues

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On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

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Machine learning for predicting enantioselectivity in chiral phosphoric acid-catalyzed naphthyl-indole synthesis

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Inter‐Hammett: Enhancing Interpretability in Hammett‘s Constant Prediction via Extracting Rules

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Coming of Age of Computational Chemistry from a Resilient Past to a Promising Future

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Ligand Backbone Influence on the Enantioselectivity in the Ruthenium‐Catalyzed Direct Asymmetric Reductive Amination of Ketones with NH3/H2 Using Binaphthyl‐Substituted Phosphines

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Comparison of Machine Learning Algorithms in Screening Potential Additives to Ni/Al 2 O 3 Methanation Catalysts for Improving the Anti‐Coking Performance

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Predicting the Stereoselectivity of Chemical Transformations by Machine LearningPreprint

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Kernel Methods for Predicting Yields of Chemical ReactionsPreprint

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Baldwin and Whitehead’s Manzamine Alkaloids Biosynthesis Hy-pothesis Involves a Finely Tuned Reactivity of C3 Unit: a High-Throughput Experimentation ApproachPreprint

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Random Forest‐Based Prediction of Enantioselectivity in Thiol Addition to Imines Catalyzed by Chiral Phosphoric Acids

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AI-Driven Discovery of Asymmetric Pauson–Khand Reactions: A New Toolbox in a Synthetic Chemist’s Treasure

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Fast robust kernel regression through sign gradient descent with early stopping

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Optimising Silver-Enhanced BiFeO3 Nanoparticle Catalysts Using Random Forests and Genetic Algorithms

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Machine learning for rapid quantitative stucco phase analysis in plasterboard

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Cgip-Deepreac+ Model for Organic Chemical Reactions Based on Deep Active Learning

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Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction

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Machine Learning‐Assisted Prediction of Ground‐ and Excited‐State Redox Potentials in Iridium(III) Photocatalysts

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Data‐Driven Modeling of N,N′ ‐Dioxide/Metal‐Catalyzed Asymmetric Michael Additions

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Constructing and explaining machine learning models for the exploration and design of boron-based Lewis acids

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Mechanism‐Oriented Reaction Descriptors for Dispersity Prediction in Nitroxide‐Mediated Radical Polymerization Considering Mediator Stability

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BiML-SyMO: Bridging multi-objective optimization of catalysts and chemical systems through a bilateral machine learning framework

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Data-Integrated Elucidation of Structure–Activity Relationships toward the Rational Design of Perfluoroiodoarene-Based Halogen-Bond Donor Catalysts

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An interpretable ensemble learning framework based on Bayesian optimization for the optimal design of ammonia decomposition catalysts

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Data-guided rational design of additives for halogenation of highly fluorinated naphthalenes: integrating fluorine chemistry and machine learning

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Predicting polymer melt flow index and catalytic activity in ethylene polymerization using a pretrained transformer-based model

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Methylene Cycloalkene as a Reactive 1,3-Diene for Enantioselective Hydroamination

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Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

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Machine Learning Accelerates Crystallization for Structure Determination

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A machine learning approach to enantioselectivity prediction in cinchona-based phase transfer catalysts

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Machine learning-assisted prediction and optimization of microwave-assisted esterification of levulinic acid for ethyl levulinate production

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Cross-material catalyst discovery via deep learning

Moon, J., Yoo, S., Shim, J., Heo, S., Kim, J. H., Bootharaju, M. S., Lee, K.-S., Ryu, J., Sung, Y., & Hyeon, T. (2026) · Nature Materials · doi:10.1038/s41563-026-02622-6 · Cited by 4

Carbon Curvature Influence on Single‐Atom Catalysts for CO 2 Reduction: A Study via High‐Throughput Calculations and Machine Learning

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Automated High-Throughput Virtual Screening of Catalysts via Templated Organic Reaction Pathway Construction: A Case Study on Suzuki–Miyaura Coupling Reaction

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Construction of an Interpretable Regression Model for Yield Prediction and Mechanistic Insight Enabled by Automated Reaction Path Exploration

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Magnetoelectrocatalysis at the frontier: advanced quantum materials, mechanistic insights, and application landscapes

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Transfer Learning Combined with High-Throughput Experimentation Framework for Integrated Biorefinery

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Black-box optimization algorithms for chemical synthesis and reaction condition optimization

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Machine‐Learning Classification for the Prediction of Catalytic Activity of Organic Photosensitizers in the Nickel(II)‐Salt‐Induced Synthesis of Phenols

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The Calculation of Both Electrostatic and Van der Waals Effects to Probe the Efficiency of Solvent Extraction of Heterocyclic Aromatics from Heavy Oil

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Synthesize in a Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis

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The prediction of crystal densities of a big data set using 1D and 2D structure features

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Evolutionary features for task-specific machine-learning applications

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A general-purpose framework for chemical reaction representation with atomic correspondence and flexible condition adaptation

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C–H Aminoalkylation of 5-Membered Heterocycles: Influence of Descriptors, Data Set Size, and Data Quality on the Predictiveness of Machine Learning Models and Expansion of the Substrate Space Beyond 1,3-Azoles

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