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OpenAlex Citation Counts

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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:

Ab Initio Machine Learning in Chemical Compound Space
Bing Huang, O. Anatole von Lilienfeld
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 10001-10036
Open Access | Times Cited: 126

Showing 1-25 of 126 citing articles:

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Haoxin Mai, Tu C. Le, Dehong Chen, et al.
Chemical Reviews (2022) Vol. 122, Iss. 16, pp. 13478-13515
Closed Access | Times Cited: 265
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, et al.
Patterns (2022) Vol. 3, Iss. 10, pp. 100588-100588
Open Access | Times Cited: 155
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, Matthias Rupp
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 116
The central role of density functional theory in the AI age
Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Science (2023) Vol. 381, Iss. 6654, pp. 170-175
Open Access | Times Cited: 100
Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning
Jianwen Liu, Wenzhi Luo, Lei Wang, et al.
Advanced Functional Materials (2022) Vol. 32, Iss. 17
Closed Access | Times Cited: 77
Accelerated chemical science with AI
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, et al.
Digital Discovery (2023) Vol. 3, Iss. 1, pp. 23-33
Open Access | Times Cited: 43
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
Oliver T. Unke, Martin Stöhr, Stefan Ganscha, et al.
Science Advances (2024) Vol. 10, Iss. 14
Open Access | Times Cited: 28
ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT
Jyotirmoy Deb, Lakshi Saikia, Kripa Dristi Dihingia, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 799-811
Closed Access | Times Cited: 19
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 18
Machine learning based energy-free structure predictions of molecules, transition states, and solids
Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 91
Machine learning for design principles for single atom catalysts towards electrochemical reactions
Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain, et al.
Journal of Materials Chemistry A (2022) Vol. 10, Iss. 29, pp. 15309-15331
Open Access | Times Cited: 68
High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications
Xijun Wang, Yunfei Gao, Emily Krzystowczyk, et al.
Energy & Environmental Science (2022) Vol. 15, Iss. 4, pp. 1512-1528
Open Access | Times Cited: 67
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
Multi-fidelity prediction of molecular optical peaks with deep learning
Kevin P. Greenman, William H. Green, Rafael Gómez‐Bombarelli
Chemical Science (2022) Vol. 13, Iss. 4, pp. 1152-1162
Open Access | Times Cited: 59
Chemical Multiverse: An Expanded View of Chemical Space
José L. Medina‐Franco, Ana L. Chávez‐Hernández, Edgar López‐López, et al.
Molecular Informatics (2022) Vol. 41, Iss. 11
Open Access | Times Cited: 52
Transition1x - a dataset for building generalizable reactive machine learning potentials
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 52
REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems
Yaolong Zhang, Junfan Xia, Bin Jiang
The Journal of Chemical Physics (2022) Vol. 156, Iss. 11
Open Access | Times Cited: 48
AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs
Dylan M. Anstine, R.I. Zubatyuk, Olexandr Isayev
(2023)
Open Access | Times Cited: 32
Universal machine learning for the response of atomistic systems to external fields
Yaolong Zhang, Bin Jiang
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 31
Integrated Molecular Modeling and Machine Learning for Drug Design
Song Xia, Eric Chen, Yingkai Zhang
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 21, pp. 7478-7495
Open Access | Times Cited: 30
Ultra-fast interpretable machine-learning potentials
Stephen Xie, Matthias Rupp, Richard G. Hennig
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 28
Open-Source Machine Learning in Computational Chemistry
Alexander Hagg, Karl N. Kirschner
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 15, pp. 4505-4532
Open Access | Times Cited: 28
Deep learning metal complex properties with natural quantum graphs
Hannes Kneiding, Ruslan Lukin, Lucas Lang, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 618-633
Open Access | Times Cited: 22
Some Recent Advances in Density-Based Reactivity Theory
He Xin, Meng Li, Chunying Rong, et al.
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 7, pp. 1183-1196
Closed Access | Times Cited: 13
Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes
Sheng Bi, Lisanne Knijff, Xiliang Lian, et al.
ACS Nano (2024) Vol. 18, Iss. 31, pp. 19931-19949
Open Access | Times Cited: 13
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