Papers citing A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
Accounts of Chemical Research · 2023
Sukriti Singh, Raghavan B. Sunoj
Read original paper ↗ ·Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis—Challenges and Opportunities
Molecules · 2023
Choon Wee Kee
Read original paper ↗ ·High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation
Journal of the American Chemical Society · 2023
Drew Behrendt, Sayan Banerjee, Cole Clark, Andrew M. Rappe
Read original paper ↗ ·Genetic Algorithms for the Discovery of Homogeneous Catalysts
CHIMIA International Journal for Chemistry · 2023
Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Rubén Laplaza, Clémence Corminbœuf
Read original paper ↗ ·Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge
Nature Communications · 2023
Shu-Wen Li, Li‐Cheng Xu, Cheng Zhang, Shuo‐Qing Zhang, Xin Hong
Read original paper ↗ ·ML meets MLn: Machine learning in ligand promoted homogeneous catalysis
Artificial Intelligence Chemistry · 2023
Jonathan D. Hirst, Samuel Boobier, Jennifer Coughlan, Jessica Streets, Philippa L. Jacob, O. Pugh, Ender Özcan, Simon Woodward
Read original paper ↗ ·Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction
ACS Central Science · 2023
Abbigayle E. Cuomo, Sebastian Ibarraran, Sanil Sreekumar, Haote Li, Jungmin Eun, Jan Paul Menzel, Pengpeng Zhang, Frédéric G. Buono, Jinhua J. Song, Robert H. Crabtree, Víctor S. Batista, Timothy R. Newhouse
Read original paper ↗ ·MIM-ML: A Novel Quantum Chemical Fragment-Based Random Forest Model for Accurate Prediction of NMR Chemical Shifts of Nucleic Acids
Journal of Chemical Theory and Computation · 2023
Sruthy K. Chandy, Krishnan Raghavachari
Read original paper ↗ ·Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions
The Journal of Physical Chemistry A · 2023
Shilpa Shilpa, Gargee Kashyap, Raghavan B. Sunoj
Read original paper ↗ ·Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe–N–C Catalysts
The Journal of Physical Chemistry Letters · 2023
Qingxin Wang, Xinrui Liu, Siying Tao, Haining Wang, Shanfu Lu, Yan Xiang, Jing Zhang
Read original paper ↗ ·Machine learning approach for predicting the yield of pyrroles and dipyrromethanes condensation reactions with aldehydes
Journal of Computational Science · 2023
Dmitriy M. Makarov, Michail M. Lukanov, Aleksey I. Rusanov, Nugzar Zh. Mamardashvili, Alexander Andreevich Ksenofontov
Read original paper ↗ ·Intelligent design and synthesis of energy catalytic materials
Fundamental Research · 2023
Linkai Han, Zhonghua Xiang
Read original paper ↗ ·Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions
Catalysis Science & Technology · 2024
Carmen Martínez-Alonso, Valentín Vassilev-Galindo, Benjamin M. Comer, Frank Abild‐Pedersen, Kirsten T. Winther, Javier LLorca
Read original paper ↗ ·CatScore: evaluating asymmetric catalyst design at high efficiency
Digital Discovery · 2024
Bing Yan, Kyunghyun Cho
Read original paper ↗ ·Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts
Chemical Science · 2024
Adarsh V. Kalikadien, Cecile Valsecchi, Robbert van Putten, Tor Maes, Mikko Muuronen, Natalia Dyubankova, Laurent Lefort, Evgeny A. Pidko
Read original paper ↗ ·Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Artificial Intelligence Chemistry · 2024
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan, Xuefeng Jiang
Read original paper ↗ ·Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Advanced Materials · 2024
Vera A. Kuznetsova, Áine Coogan, Dmitry Botov, Yulia A. Gromova, Elena V. Ushakova, Yurii K. Gun’ko
Read original paper ↗ ·Paving the road towards automated homogeneous catalyst design
ChemPlusChem · 2024
Adarsh V. Kalikadien, Adrian Mirza, Aydin Najl Hossaini, A. Sreenithya, Evgeny A. Pidko
Read original paper ↗ ·Advances in machine learning with chemical language models in molecular property and reaction outcome predictions
Journal of Computational Chemistry · 2024
Manajit Das, Ankit Ghosh, Raghavan B. Sunoj
Read original paper ↗ ·Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction
ACS Catalysis · 2024
H. Ray Kelly, Sanil Sreekumar, Vidhyadhar Manee, Abbigayle E. Cuomo, Timothy R. Newhouse, Víctor S. Batista, Frédéric G. Buono
Read original paper ↗ ·From Single Metals to High‐Entropy Alloys: How Machine Learning Accelerates the Development of Metal Electrocatalysts
Advanced Functional Materials · 2024
Xinyu Fan, Letian Chen, Dulin Huang, Yun Tian, Xu Zhang, Menggai Jiao, Zhen Ping Zhou
Read original paper ↗ ·Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns: A Case Study of Rh-Catalyzed Acrylate Hydrogenation
The Journal of Physical Chemistry C · 2024
Margareth S. Baidun, Adarsh V. Kalikadien, Laurent Lefort, Evgeny A. Pidko
Read original paper ↗ ·Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction
Journal of the American Chemical Society · 2024
Oliver Pereira, Marcel Ruth, Dennis Gerbig, Raffael Christoph Wende, Peter R. Schreiner
Read original paper ↗ ·Discovery of wurtzite solid solutions with enhanced piezoelectric response using machine learning
Physical Review Materials · 2024
Drew Behrendt, Sayan Banerjee, Jiahao Zhang, Andrew M. Rappe
Read original paper ↗ ·Universal descriptors of quasi transition states for small-data-driven asymmetric catalysis prediction in machine learning model
Cell Reports Physical Science · 2024
Guan-Ming Chen, Zi-Hao Ye, Zhiming Li, Junliang Zhang
Read original paper ↗ ·Deep Kernel learning for reaction outcome prediction and optimization
Communications Chemistry · 2024
Sukriti Singh, José Miguel Hernández-Lobato
Read original paper ↗ ·AI for organic and polymer synthesis
Science China Chemistry · 2024
Xin Hong, Qi Yang, Kuangbiao Liao, Jianfeng Pei, Mao Chen, Fanyang Mo, Hua Lu, Wen-Bin Zhang, Haisen Zhou, Jiaxiao Chen, Lebin Su, Shuo‐Qing Zhang, Siyuan Liu, Xu Huang, Yizhou Sun, Yuxiang Wang, Zexi Zhang, Zhunzhun Yu, Sanzhong Luo, Xuefeng Fu, Shu‐Li You
Read original paper ↗ ·When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Journal of the American Chemical Society · 2024
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, Kevin Spiekermann, Yi‐Pei Li, William H. Green
Read original paper ↗ ·6‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization
Angewandte Chemie · 2024
Lanting Xu, Jiazhou Zhu, Xiaodong Shen, Jialei Chai, Lei Shi, Bing Wu, Wei Li, Dawei Ma
Read original paper ↗ ·6‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization
Angewandte Chemie International Edition · 2024
Lanting Xu, Lanting Xu, Jiazhou Zhu, Xiaodong Shen, Jialei Chai, Lei Shi, Bin Wu, Wei Li, Wei Li, Dawei Ma
Read original paper ↗ ·ROBERT : Bridging the Gap Between Machine Learning and Chemistry
Wiley Interdisciplinary Reviews Computational Molecular Science · 2024
David Dalmau, Juan V. Alegre‐Requena
Read original paper ↗ ·Reinforcement Learning for Improving Chemical Reaction Performance
Journal of the American Chemical Society · 2024
Ajnabiul Hoque, Mihir Surve, Shivaram Kalyanakrishnan, Raghavan B. Sunoj
Read original paper ↗ ·Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ G ≠ versus Enantiomeric Excess
Angewandte Chemie International Edition · 2024
Marcel Ruth, Tobias Gensch, Peter R. Schreiner
Read original paper ↗ ·Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess
Angewandte Chemie · 2024
Marcel Ruth, Tobias Gensch, Peter R. Schreiner
Read original paper ↗ ·Machine Learned Potential Enables Molecular Dynamics Simulation to Predict the Experimental Branching Ratios in the NO Release Channel of Nitroaromatic Compounds
The Journal of Physical Chemistry A · 2024
Pooja Sharma, Prahlad Roy Chowdhury, Amber Jain, G. Naresh Patwari
Read original paper ↗ ·AI-Driven Discovery of Asymmetric Pauson–Khand Reactions: A New Toolbox in a Synthetic Chemist’s Treasure
The Journal of Physical Chemistry A · 2024
Neha Rani, Rohit Kumar, Shivnath Mazumder
Read original paper ↗ ·Reticulating Crystalline Porous Materials for Asymmetric Heterogeneous Catalysis
Advanced Materials · 2024
Xing Han, Weiwei Li, Baokang Yang, Chao Jiang, Zan Qu, Haomiao Xu, Yan Liu, Yong Cui
Read original paper ↗ ·A machine learning-driven prediction of Hammett constants using quantum chemical and structural descriptors
Physical Chemistry Chemical Physics · 2025
Vaneet Saini, Ranjeet Kumar
Read original paper ↗ ·Connecting the complexity of stereoselective synthesis to the evolution of predictive tools
Chemical Science · 2025
Jiajing Li, Jolene P. Reid
Read original paper ↗ ·Molecular Machine Learning Approach to Enantioselective C–H Bond Activation Reactions: From Generative AI to Experimental Validation
Chemical Science · 2025
Ajnabiul Hoque, Taiwei Chang, Jin‐Quan Yu, Raghavan B. Sunoj
Read original paper ↗ ·Database Construction for the Virtual Screening of the Ruthenium-Catalyzed Hydrogenation of Ketones
The Journal of Organic Chemistry · 2025
Haruno Nakajima, Chihaya Murata, Naoki Noto, Susumu Saito
Read original paper ↗ ·Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach
Molecules · 2025
Eduardo Aguilar-Bejarano, Viraj Deorukhkar, Simon Woodward
Read original paper ↗ ·Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis
iScience · 2025
Eduardo Aguilar-Bejarano, Ender Özcan, Raja K. Rit, Hongyi Li, Hon Wai Lam, Jonathan C. Moore, Simon Woodward, Grazziela Patrocinio Figueredo
Read original paper ↗ ·Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis
Chemical Reviews · 2025
Isaiah O. Betinol, Yutao Kuang, Brian P. Mulley, Jolene P. Reid
Read original paper ↗ ·Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space
ACS Catalysis · 2025
Isaiah O. Betinol, Aleksandra Demchenko, Jolene P. Reid
Read original paper ↗ ·Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation
Nature Synthesis · 2025
Xinran Chen, Zijing Zhang, Xin Hong, Lutz Ackermann
Read original paper ↗ ·Machine learning for predicting enantioselectivity in chiral phosphoric acid-catalyzed naphthyl-indole synthesis
Journal of Chemical Sciences · 2025
R. A. Oshiya, Ayan Datta
Read original paper ↗ ·The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations
The Journal of Organic Chemistry · 2025
Shannon M. Stephens, Kyle M. Lambert
Read original paper ↗ ·AI Approaches to Homogeneous Catalysis with Transition Metal Complexes
ACS Catalysis · 2025
Lucía Morán‐González, Arron L. Burnage, Ainara Nova, David Balcells
Read original paper ↗ ·Study on Phosphorus Compound/Catechol‐Catalyzed Dehydrative Amidation and Its Database Development for Machine Learning
Chemistry - A European Journal · 2025
Taiki Nagano, Dattatraya B. Bagal, Ryuga Kunisada, Haruno Nakajima, Koki Sano, Naoki Noto, Susumu Saito
Read original paper ↗ ·