Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Pascal Friederich, Gabriel dos Passos Gomes, Riccardo De Bin, et al.
Chemical Science (2020) Vol. 11, Iss. 18, pp. 4584-4601
Open Access | Times Cited: 152

Showing 1-25 of 152 citing articles:

Data-Driven Strategies for Accelerated Materials Design
Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 4, pp. 849-860
Open Access | Times Cited: 317
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 3, pp. 1205-1217
Closed Access | Times Cited: 220
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Aditya Nandy, Chenru Duan, Michael G. Taylor, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9927-10000
Closed Access | Times Cited: 213
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
Kjell Jorner, Tore Brinck, Per‐Ola Norrby, et al.
Chemical Science (2020) Vol. 12, Iss. 3, pp. 1163-1175
Open Access | Times Cited: 142
Organic reactivity from mechanism to machine learning
Kjell Jorner, Anna Tomberg, Christoph Bauer, et al.
Nature Reviews Chemistry (2021) Vol. 5, Iss. 4, pp. 240-255
Closed Access | Times Cited: 142
Data‐Driven Materials Innovation and Applications
Zhuo Wang, Zhehao Sun, Hang Yin, et al.
Advanced Materials (2022) Vol. 34, Iss. 36
Closed Access | Times Cited: 106
Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties
Liliana C. Gallegos, Guilian Luchini, Peter C. St. John, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 4, pp. 827-836
Closed Access | Times Cited: 104
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 90
Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability
Thijs Stuyver, Connor W. Coley
The Journal of Chemical Physics (2022) Vol. 156, Iss. 8
Open Access | Times Cited: 73
Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts
Simone Gallarati, Raimón Fabregat, Rubén Laplaza, et al.
Chemical Science (2021) Vol. 12, Iss. 20, pp. 6879-6889
Open Access | Times Cited: 86
Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis
Jeremy Henle, Andrew F. Zahrt, Brennan T. Rose, et al.
Journal of the American Chemical Society (2020) Vol. 142, Iss. 26, pp. 11578-11592
Closed Access | Times Cited: 82
Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning
Gabriel dos Passos Gomes, Robert Pollice, Alán Aspuru–Guzik
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 96-110
Open Access | Times Cited: 78
Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, et al.
Chem (2021) Vol. 7, Iss. 3, pp. 738-751
Open Access | Times Cited: 78
tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes
David Balcells, Bastian Bjerkem Skjelstad
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6135-6146
Open Access | Times Cited: 77
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
The Journal of Chemical Physics (2021) Vol. 155, Iss. 6
Open Access | Times Cited: 65
Deep dive into machine learning density functional theory for materials science and chemistry
Lenz Fiedler, Karan Shah, Michael Bußmann, et al.
Physical Review Materials (2022) Vol. 6, Iss. 4
Open Access | Times Cited: 65
Machine learning activation energies of chemical reactions
Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson
Wiley Interdisciplinary Reviews Computational Molecular Science (2021) Vol. 12, Iss. 4
Open Access | Times Cited: 60
New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts
Aditya Nandy, Chenru Duan, Conrad Goffinet, et al.
JACS Au (2022) Vol. 2, Iss. 5, pp. 1200-1213
Open Access | Times Cited: 52
Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling
Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, et al.
ACS Catalysis (2022) Vol. 12, Iss. 13, pp. 7773-7780
Closed Access | Times Cited: 43
%VBur index and steric maps: from predictive catalysis to machine learning
Sílvia Escayola, Naeimeh Bahri‐Laleh, Albert Poater
Chemical Society Reviews (2023) Vol. 53, Iss. 2, pp. 853-882
Open Access | Times Cited: 35
Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 34
Indolizine Synthesis through Annulation of Pyridinium 1,4‐Thiolates and Copper Carbenes: A Predictive Catalysis Approach
Roger Monreal‐Corona, Àlex Díaz‐Jiménez, Anna Roglans, et al.
Advanced Synthesis & Catalysis (2023) Vol. 365, Iss. 5, pp. 760-766
Open Access | Times Cited: 27
Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces
Xin Yang, Arghya Bhowmik, Tejs Vegge, et al.
Chemical Science (2023) Vol. 14, Iss. 14, pp. 3913-3922
Open Access | Times Cited: 25
Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions
Thijs Stuyver, Kjell Jorner, Connor W. Coley
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 24
Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge
Shu-Wen Li, Li‐Cheng Xu, Cheng Zhang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 24
Page 1 - Next Page

Scroll to top