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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 and molecular descriptors enable rational solvent selection in asymmetric catalysis
Yehia Amar, Artur M. Schweidtmann, Paul Deutsch, et al.
Chemical Science (2019) Vol. 10, Iss. 27, pp. 6697-6706
Open Access | Times Cited: 116

Showing 1-25 of 116 citing articles:

Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Takashi Toyao, Zen Maeno, Satoru Takakusagi, et al.
ACS Catalysis (2019) Vol. 10, Iss. 3, pp. 2260-2297
Closed Access | Times Cited: 433
Eutectics: formation, properties, and applications
Dongkun Yu, Zhimin Xue, Tiancheng Mu
Chemical Society Reviews (2021) Vol. 50, Iss. 15, pp. 8596-8638
Closed Access | Times Cited: 258
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: 197
Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats
Maarten R. Dobbelaere, Pieter Plehiers, Ruben Van de Vijver, et al.
Engineering (2021) Vol. 7, Iss. 9, pp. 1201-1211
Open Access | Times Cited: 191
A Brief Introduction to Chemical Reaction Optimization
Connor J. Taylor, Alexander Pomberger, Kobi Felton, et al.
Chemical Reviews (2023) Vol. 123, Iss. 6, pp. 3089-3126
Open Access | Times Cited: 183
A review of molecular representation in the age of machine learning
Daniel Wigh, Jonathan M. Goodman, Alexei A. Lapkin
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 176
Automated in Silico Design of Homogeneous Catalysts
Marco Foscato, Vidar R. Jensen
ACS Catalysis (2020) Vol. 10, Iss. 3, pp. 2354-2377
Open Access | Times Cited: 172
Machine Learning in Catalysis, From Proposal to Practicing
Wenhong Yang, Timothy Tizhe Fidelis, Wen‐Hua Sun
ACS Omega (2019) Vol. 5, Iss. 1, pp. 83-88
Open Access | Times Cited: 156
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: 148
Machine Learning in Chemical Engineering: A Perspective
Artur M. Schweidtmann, Erik Esche, Asja Fischer, et al.
Chemie Ingenieur Technik (2021) Vol. 93, Iss. 12, pp. 2029-2039
Open Access | Times Cited: 144
Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors
Li‐Tao Zhu, Xizhong Chen, Bo Ouyang, et al.
Industrial & Engineering Chemistry Research (2022) Vol. 61, Iss. 28, pp. 9901-9949
Open Access | Times Cited: 133
Toward Machine Learning-Enhanced High-Throughput Experimentation
Natalie S. Eyke, Brent A. Koscher, Klavs F. Jensen
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 120-132
Open Access | Times Cited: 106
Accelerated dinuclear palladium catalyst identification through unsupervised machine learning
Julian A. Hueffel, Theresa Sperger, Ignacio Funes‐Ardoiz, et al.
Science (2021) Vol. 374, Iss. 6571, pp. 1134-1140
Closed Access | Times Cited: 106
Replacement of Less-Preferred Dipolar Aprotic and Ethereal Solvents in Synthetic Organic Chemistry with More Sustainable Alternatives
Andrew Jordan, Callum G. J. Hall, Lee R. Thorp, et al.
Chemical Reviews (2022) Vol. 122, Iss. 6, pp. 6749-6794
Open Access | Times Cited: 102
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: 100
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: 69
Accelerated Chemical Reaction Optimization Using Multi-Task Learning
Connor J. Taylor, Kobi Felton, Daniel Wigh, et al.
ACS Central Science (2023) Vol. 9, Iss. 5, pp. 957-968
Open Access | Times Cited: 59
ASSESSMENT OF DATA COLLECTION TECHNIQUES IN MANUFACTURING AND MECHANICAL ENGINEERING THROUGH MACHINE LEARNING MODELS

Deleted Journal (2023) Vol. 2, Iss. 4, pp. 15-26
Open Access | Times Cited: 40
N,N‐Dimethyl Formamide European Restriction Demands Solvent Substitution in Research and Development
James Sherwood, Fernando Alberício, Beatriz G. de la Torre
ChemSusChem (2024) Vol. 17, Iss. 8
Open Access | Times Cited: 16
Embracing data science in catalysis research
Manu Suvarna, Javier Pérez‐Ramírez
Nature Catalysis (2024) Vol. 7, Iss. 6, pp. 624-635
Closed Access | Times Cited: 16
Automated self-optimisation of multi-step reaction and separation processes using machine learning
Adam D. Clayton, Artur M. Schweidtmann, Graeme Clemens, et al.
Chemical Engineering Journal (2019) Vol. 384, pp. 123340-123340
Open Access | Times Cited: 128
Chlorinated Solvents: Their Advantages, Disadvantages, and Alternatives in Organic and Medicinal Chemistry
Andrew Jordan, Patrick Stoy, Helen F. Sneddon
Chemical Reviews (2020) Vol. 121, Iss. 3, pp. 1582-1622
Open Access | Times Cited: 98
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
Cost, performance prediction and optimization of a vanadium flow battery by machine-learning
Tianyu Li, Feng Xing, Tao Liu, et al.
Energy & Environmental Science (2020) Vol. 13, Iss. 11, pp. 4353-4361
Closed Access | Times Cited: 84
Automated Experimentation Powers Data Science in Chemistry
Yao Shi, Paloma L. Prieto, Tara Zepel, et al.
Accounts of Chemical Research (2021) Vol. 54, Iss. 3, pp. 546-555
Closed Access | Times Cited: 82
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