Papers citing Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
3DReact: Geometric Deep Learning for Chemical Reactions
arXiv (Cornell University) · 2023
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminbœuf
Read original paper ↗ ·% V Bur index and steric maps: from predictive catalysis to machine learning
Chemical Society Reviews · 2023
Sílvia Escayola, Naeimeh Bahri‐Laleh, Albert Poater
Read original paper ↗ ·Deep Graph Learning for Molecules and Materials
Nordic Machine Intelligence · 2023
Hannes Kneiding, David Balcells
Read original paper ↗ ·Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Digital Discovery · 2024
Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clémence Corminbœuf
Read original paper ↗ ·Every atom counts: predicting sites of reaction based on chemistry within two bonds
Digital Discovery · 2024
Ching Ching Lam, Jonathan M. Goodman
Read original paper ↗ ·Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity
Chemical Science · 2024
Bo Yang, Anthony J. Schaefer, Brooke L. Small, Julie A. Hopkins Leseberg, Steven M. Bischof, Michael S. Webster‐Gardiner, Daniel H. Ess
Read original paper ↗ ·Molecular modelling of encapsulation and reactivity within metal-organic cages (MOCs)
Advances in catalysis · 2024
Mercè Alemany‐Chavarria, Gantulga Norjmaa, Giuseppe Sciortino, Gregori Ujaque
Read original paper ↗ ·OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion
Digital Discovery · 2024
François Cornet, Bardi Benediktsson, Bjarke Hastrup, Mikkel Nørgaard Schmidt, Arghya Bhowmik
Read original paper ↗ ·Tailoring phosphine ligands for improved C–H activation: insights from Δ-machine learning
Digital Discovery · 2024
Tianbai Huang, Robert Geitner, Alexander Croy, Stefanie Gräfe
Read original paper ↗ ·Toward AI/ML-assisted discovery of transition metal complexes
Annual reports in computational chemistry · 2024
Hongni Jin, Kenneth M. Merz
Read original paper ↗ ·Tuning the steric hindrance of alkylamines: a predictive model of steric editing of planar amines
Chemical Science · 2024
Michele Tomasini, Maria Voccia, Lucia Caporaso, Michal Szostak, Albert Poater
Read original paper ↗ ·Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning
ChemCatChem · 2024
S. Macé, Yingjian Xu, Bao N. Nguyen
Read original paper ↗ ·Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle
Journal of Cleaner Production · 2024
Zi-Kai Lin, Chao-Sheng Li, Rong-Rong Cai, Yan-Qiu Long
Read original paper ↗ ·Machine Learning Models for Predicting Zirconocene Properties and Barriers
Journal of Chemical Information and Modeling · 2024
Justin Kyle Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison Lambert, G.R. Lief, Qing Yang, Daniel H. Ess
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 ↗ ·On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set
ACS Omega · 2024
Hiroaki Okada, Satoshi Maeda
Read original paper ↗ ·Unveiling similarities and differences in oxidation processes of oxidants and derived reactive oxygen species through machine learning interpretation of oxidation rate constants
Separation and Purification Technology · 2024
Zuhong Lin, Siyuan Pan, Haipu Li, Jingjing Yao
Read original paper ↗ ·Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability
The Journal of Physical Chemistry A · 2024
Sai Mahit Vadaddi, Qiyuan Zhao, Brett Matthew Savoie
Read original paper ↗ ·Directional multiobjective optimization of metal complexes at the billion-system scale
Nature Computational Science · 2024
Hannes Kneiding, Ainara Nova, David Balcells
Read original paper ↗ ·Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields
Journal of Chemical Theory and Computation · 2024
Ignacio Migliaro, Thomas R. Cundari
Read original paper ↗ ·Recent computational insights into hydrogen storage by MXene-based materials and shedding light on the storage mechanism
Journal of Energy Storage · 2024
Türkan Kopaç
Read original paper ↗ ·3DReact: Geometric Deep Learning for Chemical Reactions
Journal of Chemical Information and Modeling · 2024
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminbœuf
Read original paper ↗ ·Integrating digital chemistry within the broader chemistry community
Trends in Chemistry · 2024
David Dalmau, Juan V. Alegre‐Requena
Read original paper ↗ ·Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks
Separation and Purification Technology · 2024
Zuhong Lin, Jiarong Chen, Ying Ze Fang, Shihai Deng, Haipu Li, Ying Yang, Jingjing Yao
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 ↗ ·Machine-learning models for atom-diatom reactions across isotopologues
Physical Review A · 2024
Daniel Julian, Rian Koots, Jesús Pérez‐Ríos
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 ↗ ·Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis
ACS Catalysis · 2024
Jonas B. Ekeli, Marco Foscato, Christian O. Blanco, Giovanni Occhipinti, Deryn E. Fogg, Vidar Remi Jensen
Read original paper ↗ ·AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
Journal of Chemical Information and Modeling · 2024
Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, David Balcells
Read original paper ↗ ·Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport
npj Computational Materials · 2024
Linfeng Yu, Kexin Dong, Qi Yang, Yi Zhang, Zheyong Fan, Xiong Zheng, Huimin Wang, Zhenzhen Qin, Guangzhao Qin
Read original paper ↗ ·Computational tools for the prediction of site- and regioselectivity of organic reactions
Chemical Science · 2025
Lukas M. Sigmund, Michele Assante, Magnus J. Johansson, Per‐Ola Norrby, Kjell Jorner, Mikhail Kabeshov
Read original paper ↗ ·Predicting hydrogen atom transfer energy barriers using Gaussian process regression
Digital Discovery · 2025
Evgeni Ulanov, Ghulam A. Qadir, Kai Riedmiller, Pascal Friederich, Frauke Gräter
Read original paper ↗ ·Screening Diels–Alder reaction space to identify candidate reactions for self-healing polymer applications
Digital Discovery · 2025
Maxime Ferrer, Bowen Deng, Javier Emilio Alfonso Ramos, Thijs Stuyver
Read original paper ↗ ·Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes
Digital Discovery · 2025
Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort, Evgeny A. Pidko
Read original paper ↗ ·Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides
Asian Journal of Organic Chemistry · 2025
Michele Tomasini, Michal Szostak, Albert Poater
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 ↗ ·High-Throughput Optimization of a High-Pressure Catalytic Reaction
Journal of Chemical Information and Modeling · 2025
Yusuke Tanabe, Hiroki Sugisawa, Tomohisa Miyazawa, Kazuhiro Hotta, Kazuya Shiratori, Tadahiro Fujitani
Read original paper ↗ ·Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions
International Journal of Quantum Chemistry · 2025
Guo‐Jin Cao
Read original paper ↗ ·Applications of density functional theory and machine learning in nanomaterials: A review
Next Materials · 2025
Nangamso Nathaniel Nyangiwe
Read original paper ↗ ·Machine Learning Transition State Geometries and Applications in Reaction Property Prediction
Wiley Interdisciplinary Reviews Computational Molecular Science · 2025
Isaac W. Beaglehole, Miles J. Pemberton, Elliot H. E. Farrar, Matthew N. Grayson
Read original paper ↗ ·Machine‐Learning‐Based Design of Metallocene Catalysts for Controlled Olefin Copolymerization
Chemistry - A European Journal · 2025
Yongjun Kim, Yeonjoon Kim, Hyeonsu Kim, Sungwoo Kang, Jaewook Kim, Kyung-Hoon Lee, Wook Jeong, Won Jong Lee, Ho Ryu, Kyungwoo Kim, Woo Youn Kim
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 ↗ ·Improving generative inverse design of molecular catalysts in small data regime
Machine Learning Science and Technology · 2025
François Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel Nørgaard Schmidt, Arghya Bhowmik
Read original paper ↗ ·Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships
Journal of Chemical Information and Modeling · 2025
Heather J. Kulik
Read original paper ↗ ·Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
Venue not supplied by source · 2025
Thijs Stuyver, Javier Emilio Alfonso Ramos
Read original paper ↗ ·Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics
Venue not supplied by source · 2025
David Dalmau, Juan V. Alegre‐Requena
Read original paper ↗ ·Predicting Reactivity with Machine Learning
Venue not supplied by source · 2025
Lauriane Jacot‐Descombes, Kjell Jorner
Read original paper ↗ ·Building hybrid AI models in chemical engineering: A tutorial review
Computers & Chemical Engineering · 2025
Arijit Chakraborty, Naz Pinar Taskiran, Rishab Kottooru, Vipul Mann, Venkat Venkatasubramanian
Read original paper ↗ ·Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models
Journal of the American Chemical Society · 2025
Jieyu Lü, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia, Chenru Duan
Read original paper ↗ ·Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations
The Journal of Organic Chemistry · 2025
Angus B. Keto, Elizabeth H. Krenske
Read original paper ↗ ·