Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

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Catalytic Markovnikov Hydrothiolation of Dehydroamino Acids: Unified Total Synthesis of Enteropeptin Sactipeptides

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Empowering Reactivity Predictions through Noise-Based Data Augmentation

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Reinforcement Learning for Improving Chemical Reaction Performance

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Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions

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Understanding Conformation Importance in Data-Driven Property Prediction Models

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A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning

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Facile and Low-Temperature Synthesis of γ‑Fe2O3 Nanoparticles with Thermally Stable Ferrimagnetism for Use in Magnetic Recording Tapes

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A Conformer‐Dependent, Quantitative Quadrant Model

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Electro‐Descriptors for the Performance Prediction of Electro‐Organic Synthesis

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Molecular field analysis for data-driven molecular design in asymmetric catalysis

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Computational molecular refinement to enhance enantioselectivity by reinforcing hydrogen bonding interactions in major reaction pathway

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Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction

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Computational methods for asymmetric catalysis

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A review: Machine learning for strain sensor-integrated soft robots

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Genetic Algorithms for the Discovery of Homogeneous Catalysts

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Revealing Transition State Stabilization in Organocatalytic Ring‐Opening Polymerization Using Data Science

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Rational design of catalysts for lithium–sulfur batteries based on descriptors: progress and prospects

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Advances in Gasoline Hydrodesulfurization Catalysts: The Role of Structure–Activity Relationships and Machine Learning Approaches

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Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts

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Application of nanozymes in problematic biofilm control: progress, challenges and prospects

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Beyond predefined ligand libraries: a genetic algorithm approach for de novo discovery of catalysts for the Suzuki coupling reactions

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Accelerated Design of Fenton‐Like Copper Single‐Atom Catalysts by Adaptive Learning with Genetic Programming

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Machine Learning for Developing Sustainable Polymers

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Prediction Method of Driving Strategy of High-Power IGBT Module Based on MEA-BP Neural Network

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Identifying the true origins of selectivity in chiral phosphoric acid catalyzed N -acyl-azetidine desymmetrizations

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Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow

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Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**

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Correction: Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence

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

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AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications

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Random Forest Regression to Predict Catalyst Deactivation in Industrial Catalytic Process

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Putting Chemical Knowledge to Work in Machine Learning for Reactivity

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Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

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Universal descriptors of quasi transition states for small-data-driven asymmetric catalysis prediction in machine learning model

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Virtual Ligand-Assisted Optimization: A Rational Strategy for Ligand Engineering

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molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction

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A Genetic Algorithm based Feature Optimization method for Citrus HLB Disease Detection using Machine Learning

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

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Statistical Analysis of Catalytic Performance in Ethylene/Methyl Acrylate Copolymerization Using Palladium/Phosphine-Sulfonate Catalysts

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Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra

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Up up down down left right left right B A Start for the catalytic hackers of programmable materials

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Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation

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Identifying the geospatial relationship of surface ozone pollution in China: Implications for key pollution control regions

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Exploring the Advanced Synthesis Strategies and Biomedical Applications of Iron Oxide-Based Nanozymes: A Comprehensive Review

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First principles and interpretable machine learning aided the exploration of defective MXene for nitrogen reduction reaction

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Predicting rare earth extraction efficiency with organophosphorus ligands: A data-driven machine learning approach

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High-throughput transient photoluminescence spectrometer for deep learning of thermally activated delayed fluorescence materials

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Accelerated Electrosynthesis Development Enabled by High-Throughput Experimentation

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Quantum Chemistry Meets Machine Learning

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The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal ComplexesPreprint

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Accelerated end-to-end chemical synthesis development with large language models

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Tuning the O p band center in Co3O4 catalysts by Fe doping for Switching lattice Oxygen-Mediated mechanism in Acidic oxygen evolution reaction

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Reaction-conditioned generative model for catalyst design and optimization with CatDRX

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Design strategies for dual-atom and multi-atom catalysts: Unlocking synergistic interactions in carbon-based electrocatalysis

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Determination of tool tip steady-state temperature in dry turning process based on artificial neural network

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Development of Computational Chemistry and Application of Computational Methods

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High-throughput computational workflow for ligand discovery in catalysis with the CSD

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Predicting ruthenium catalysed hydrogenation of esters using machine learning

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Density Matrix-Based Features as Descriptors for Oxygen Reduction and Evolution Catalysts

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Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection

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Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis

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Transfer learning-based prediction and evaluation for ionic osmotic energy conversion under concentration and temperature gradients

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Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers

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The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis

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The Promise and Pitfalls of Artificial Intelligence in the Evaluation of Synthetic “Greenness”

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

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Advances in predictive chemistry enable a multi-scale rational design approach for biofuels with advantaged properties

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Screening of steam-reforming catalysts using unsupervised machine learning

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CALCULATION OF FRACTAL DIMENSION BASED ON ARTIFICIAL NEURAL NETWORK AND ITS APPLICATION FOR MACHINED SURFACES

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AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs

Yang, L., Guo, Q., & Zhang, L. (2024) · Chemical Communications · doi:10.1039/d4cc01892c · Cited by 12

Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space

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Perioperative tislelizumab plus lenvatinib treatment for resectable hepatocellular carcinoma at high risk of recurrence: single-arm phase II trial

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Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

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Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation

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Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

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Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design

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The Diversity and Evolution of Chiral Brønsted Acid Structures

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Data Science Guiding Analysis of Organic Reaction Mechanism and Prediction

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Connecting the complexity of stereoselective synthesis to the evolution of predictive tools

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An active representation learning method for reaction yield prediction with small-scale data

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Surface Nanostructures of Pt-Compositionally Complex Alloy Single-Crystal Model Catalyst Surfaces for Improved Oxygen Reduction Reaction: Machine-Learning-Assisted Exploration

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