Papers citing Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Machine learning studies on asymmetric relay Heck reaction—Potential avenues for reaction development
The Journal of Chemical Physics · 2022
Manajit Das, Pooja Sharma, Raghavan B. Sunoj
Read original paper ↗ ·Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study
ACS Omega · 2022
Harada Yuki, Makoto Hatakeyama, Shuichi Maeda, Qi Gao, Kenichi Koizumi, Yuki Sakamoto, Yuuki Ono, Shinichiro Nakamura
Read original paper ↗ ·Machine intelligence for chemical reaction space
Wiley Interdisciplinary Reviews Computational Molecular Science · 2022
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, Charlotte Bunne, Andreas Krause, Clémence Corminbœuf, Teodoro Laino
Read original paper ↗ ·Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis
ACS Catalysis · 2022
Wataru Matsuoka, Yu Harabuchi, Satoshi Maeda
Read original paper ↗ ·Machine Learning for the Discovery, Design, and Engineering of Materials
Annual Review of Chemical and Biomolecular Engineering · 2022
Chenru Duan, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Deep dive into machine learning density functional theory for materials science and chemistry
Physical Review Materials · 2022
Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi
Read original paper ↗ ·Data‐Driven Materials Innovation and Applications
Advanced Materials · 2022
Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin, Xue‐Feng Yu
Read original paper ↗ ·New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts
JACS Au · 2022
Aditya Nandy, Chenru Duan, Conrad Goffinet, Heather J. Kulik
Read original paper ↗ ·Computational Catalyst Design with Data–Driven Tools – General Approaches and Applications
Elsevier eBooks · 2022
Jolene P. Reid
Read original paper ↗ ·Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks
ACS symposium series · 2022
Jon Paul Janet
Read original paper ↗ ·Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling
ACS Catalysis · 2022
Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, Yam N. Timsina, Guolin Xu, Ben W. Glasspoole, Matthew S. Sigman
Read original paper ↗ ·Mechanistic Inference from Statistical Models at Different Data-Size Regimes
ACS Catalysis · 2022
Danilo Machado Lustosa, Anat Milo
Read original paper ↗ ·Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr
ACS Applied Materials & Interfaces · 2022
Zhiyuan Zhang, Min Cheng, Xinyi Xiao, Kexin Bi, Ting Song, Kong‐Qiu Hu, Yiyang Dai, Li Zhou, Chong Liu, Xu Ji, Wei‐Qun Shi
Read original paper ↗ ·Physics-based representations for machine learning properties of chemical reactions
Machine Learning Science and Technology · 2022
Puck van Gerwen, Alberto Fabrizio, Matthew D. Wodrich, Clémence Corminbœuf
Read original paper ↗ ·A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System
ACS Catalysis · 2022
Edward Miller, Binh Khanh Mai, Jacquelyne A. Read, William C. Bell, Jeffrey S. Derrick, Peng Liu, F. Dean Toste
Read original paper ↗ ·Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe8Me12– and Related Clusters: A Computational Study
ACS Catalysis · 2022
Andrea Darù, Carlos Martín−Fernández, Jeremy Noel Harvey
Read original paper ↗ ·Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors
Journal of Chemical Theory and Computation · 2022
Stefan Gugler, Markus Reiher
Read original paper ↗ ·Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model
Cell Reports Physical Science · 2022
Shenggan Luo, Lanxuan Liu, Chu-Jun Lyu, Byu Ri Sim, Yihan Liu, Haifan Gong, Yao Nie, Yi‐Lei Zhao
Read original paper ↗ ·Data-driven ligand field exploration of Fe( iv )–oxo sites for C–H activation
Inorganic Chemistry Frontiers · 2022
Grier M. Jones, Brett A. Smith, Justin Kyle Kirkland, Konstantinos D. Vogiatzis
Read original paper ↗ ·Deep learning metal complex properties with natural quantum graphs
Digital Discovery · 2023
Hannes Kneiding, Ruslan Yu. Lukin, Lucas Lang, Simen Sommerfelt Reine, Thomas Bondo Pedersen, Riccardo De Bin, David Balcells
Read original paper ↗ ·Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions
Digital Discovery · 2023
Oliver Tobias Schilter, Alain C. Vaucher, Philippe Schwaller, Teodoro Laino
Read original paper ↗ ·Low-cost machine learning prediction of excited state properties of iridium-centered phosphors
Chemical Science · 2023
Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik
Read original paper ↗ ·Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach
Digital Discovery · 2023
Samuel G. Espley, Elliot H. E. Farrar, David Buttar, Simone Tomasi, Matthew N. Grayson
Read original paper ↗ ·Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces
Chemical Science · 2023
Xin Li Yang, Arghya Bhowmik, Tejs Vegge, Heine Anton Hansen
Read original paper ↗ ·Predicting ruthenium catalysed hydrogenation of esters using machine learning
Digital Discovery · 2023
Challenger Mishra, Niklas von Wolff, Abhinav Tripathi, Claire N. Brodie, Neil David Lawrence, Aditya Ravuri, Éric A. G. Brémond, Annika Preiss, Amit Kumar
Read original paper ↗ ·Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights
The Journal of Chemical Physics · 2023
Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge, Pavlo O. Dral
Read original paper ↗ ·Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions
Scientific Data · 2023
Thijs Stuyver, Kjell Jorner, Connor W. Coley
Read original paper ↗ ·Indolizine Synthesis through Annulation of Pyridinium 1,4‐Thiolates and Copper Carbenes: A Predictive Catalysis Approach
Advanced Synthesis & Catalysis · 2023
Roger Monreal‐Corona, Àlex Díaz‐Jiménez, Anna Roglans, Albert Poater, Anna Pla‐Quintana
Read original paper ↗ ·Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations
The Journal of Physical Chemistry A · 2023
Xabier García-Andrade, Pablo García Tahoces, Jesús Pérez‐Ríos, Emilio Martı́nez-Núñez
Read original paper ↗ ·The Role of AI in Driving the Sustainability of the Chemical Industry
CHIMIA International Journal for Chemistry · 2023
Alessandra Toniato, Oliver Tobias Schilter, Teodoro Laino
Read original paper ↗ ·Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design
ACS Catalysis · 2023
Wataru Matsuoka, Yu Harabuchi, Satoshi Maeda
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 ↗ ·Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical Spaces
ChemRxiv · 2023
Hannes Kneiding, Ainara Nova, David Balcells
Read original paper ↗ ·Predicting electronic structures at any length scale with machine learning
npj Computational Materials · 2023
Lenz Fiedler, Normand A. Modine, Steve Schmerler, Dayton J. Vogel, Gabriel A Popoola, Aidan P. Thompson, Sivasankaran Rajamanickam, Attila Cangi
Read original paper ↗ ·Machine Learning-Guided Development of Trialkylphosphine Ni (I) Dimers and Applications in Site-Selective Catalysis
Journal of the American Chemical Society · 2023
Teresa M. Karl, Samir Bouayad‐Gervais, Julian A. Hueffel, Theresa Sperger, Sebastian Wellig, Sherif James Kaldas, Uladzislava Dabranskaya, Jas S. Ward, Kari T. Rissanen, Graham J. Tizzard, Franziska Schoenebeck
Read original paper ↗ ·Correlation between the Experimental and Theoretical Photoelectrochemical Response of a WO3 Electrode for Efficient Water Splitting through the Implementation of an Artificial Neural Network
Sustainability · 2023
Mamy Diaby, Asma Alimi, Afrah Bardaoui, Diogo M.F. Santos, R. Chtourou, I. Ben Assaker
Read original paper ↗ ·Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery
Chemistry - A European Journal · 2023
Nicholas Casetti, Javier Emilio Alfonso Ramos, Connor W. Coley, Thijs Stuyver
Read original paper ↗ ·ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design
ChemRxiv · 2023
Daniel H. Ess, Shusen Chen, Zack Meyer, Brendan Jensen, Alex Kraus, Allison Lambert
Read original paper ↗ ·AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials
ChemRxiv · 2023
Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, David Balcells
Read original paper ↗ ·Evolutionary Algorithms and Workflows for De Novo Catalyst Design
Elsevier eBooks · 2023
Marco Foscato, Jonas B. Ekeli, Marcello Costamagna, D. Grellscheid, Vidar Remi Jensen
Read original paper ↗ ·HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts
Journal of Chemical Information and Modeling · 2023
Ali Hashemi, Sana Bougueroua, Marie‐Pierre Gaigeot, Evgeny A. Pidko
Read original paper ↗ ·Prediction of Reaction Orthogonality using Machine Learning
ChemRxiv · 2023
Hootan Roshandel, Santiago Vargas, Amy J. Lai, Anastassia N. Alexandrova, Paula L. Diaconescu
Read original paper ↗ ·Discovery of Hemilabile Ligands Using MachineLearning
ChemRxiv · 2023
Ilia Kevlishvili, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·Reformulating Reactivity Design for Data-Efficient Machine Learning
ACS Catalysis · 2023
Toby Lewis‐Atwell, Daniel Beechey, Özgür Şimşek, Matthew N. Grayson
Read original paper ↗ ·The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory
The Journal of Chemical Physics · 2023
Jiewei Cheng, Tingwei Li, Yongyi Wang, Ahmed Hamad Ati, Qiang Sun
Read original paper ↗ ·Predictive catalysis: a valuable step towards machine learning
Trends in Chemistry · 2023
Roger Monreal‐Corona, Anna Pla‐Quintana, Albert Poater
Read original paper ↗ ·ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design
Journal of Chemical Information and Modeling · 2023
Shusen Chen, Zack Meyer, Brendan Jensen, Alex Kraus, Allison Lambert, Daniel H. Ess
Read original paper ↗ ·Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling
Journal of Catalysis · 2023
Nikolay O. Grebennikov, Daniil A. Boiko, Darya Olegovna Prima, Malena Madiyeva, Mikhail Evgenievich Minyaev, Valentine P. Ananikov
Read original paper ↗ ·Classification of Hemilabile Ligands Using Machine Learning
The Journal of Physical Chemistry Letters · 2023
Ilia Kevlishvili, Chenru Duan, Heather J. Kulik
Read original paper ↗ ·Estimation of the Surface Free Energy Components for Solid Surfaces: A Machine Learning Approach
Arabian Journal for Science and Engineering · 2023
Ebubekir Sıddık Aydın, Ibrahim Korkut, Salih Ozbay
Read original paper ↗ ·