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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:

Perspective on integrating machine learning into computational chemistry and materials science
Julia Westermayr, Michael Gastegger, Kristof T. Schütt, et al.
The Journal of Chemical Physics (2021) Vol. 154, Iss. 23
Open Access | Times Cited: 164

Showing 1-25 of 164 citing articles:

Artificial intelligence: A powerful paradigm for scientific research
Yongjun Xu, Xin Liu, Xin Cao, et al.
The Innovation (2021) Vol. 2, Iss. 4, pp. 100179-100179
Open Access | Times Cited: 854
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 123
How to validate machine-learned interatomic potentials
Joe D. Morrow, John L. A. Gardner, Volker L. Deringer
The Journal of Chemical Physics (2023) Vol. 158, Iss. 12
Open Access | Times Cited: 64
Accelerating the design of compositionally complex materials via physics-informed artificial intelligence
Dierk Raabe, Jaber Rezaei Mianroodi, Jörg Neugebauer
Nature Computational Science (2023) Vol. 3, Iss. 3, pp. 198-209
Closed Access | Times Cited: 45
Simulations in the era of exascale computing
C. S. Chang, Volker L. Deringer, Kalpana S. Katti, et al.
Nature Reviews Materials (2023) Vol. 8, Iss. 5, pp. 309-313
Open Access | Times Cited: 40
Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization
Liqianyun Xu, Feng Wu, Renjie Chen, et al.
Energy storage materials (2023) Vol. 59, pp. 102785-102785
Closed Access | Times Cited: 39
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 14
Open Access | Times Cited: 38
Relay Catalysis of Fe and Co with Multi‐Active Sites for Specialized Division of Labor in Electrocatalytic Nitrate Reduction Reaction
Hongxia Luo, Shuangjun Li, Ziyang Wu, et al.
Advanced Functional Materials (2024)
Closed Access | Times Cited: 24
Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Rizvi Syed Aal E Ali, Jiaolong Meng, Muhammad Ehtisham Ibraheem Khan, et al.
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 1, pp. 100049-100049
Open Access | Times Cited: 19
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: 15
Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 93
Artificial intelligence-enhanced quantum chemical method with broad applicability
Peikun Zheng, R.I. Zubatyuk, Wei Wu, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 80
Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges
M. Pineda, Michail Stamatakis
The Journal of Chemical Physics (2022) Vol. 156, Iss. 12
Open Access | Times Cited: 62
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: 49
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
Paul L. Houston, Chen Qu, Apurba Nandi, et al.
The Journal of Chemical Physics (2022) Vol. 156, Iss. 4
Open Access | Times Cited: 42
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
Paul L. Houston, Chen Qu, Qi Yu, et al.
The Journal of Chemical Physics (2023) Vol. 158, Iss. 4
Open Access | Times Cited: 25
Lifelong Machine Learning Potentials
Marco Eckhoff, Markus Reiher
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 12, pp. 3509-3525
Open Access | Times Cited: 22
Machine learning electronic structure methods based on the one-electron reduced density matrix
Xuecheng Shao, Lukas Paetow, Mark E. Tuckerman, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 20
The Future of Material Scientists in an Age of Artificial Intelligence
Ayman Maqsood, Chen Chen, T. Jesper Jacobsson
Advanced Science (2024) Vol. 11, Iss. 19
Open Access | Times Cited: 9
Experimentally validated inverse design of multi-property Fe-Co-Ni alloys
Shakti P. Padhy, Varun Chaudhary, Yee-Fun Lim, et al.
iScience (2024) Vol. 27, Iss. 5, pp. 109723-109723
Open Access | Times Cited: 7
Energy Landscapes in Chemical Reactions and Transport
Karl‐Michael Weitzel
ChemPhysChem (2025)
Open Access
Simple Approach to More Efficient Density Functional Theory Simulations
Mohammed Benaïssa, Tarik Ouahrani, Keisuke Hatada, et al.
Computational Condensed Matter (2025), pp. e01012-e01012
Closed Access
Comprehensive Study on Synthesis, Quantum Chemical Calculations, Molecular Modeling Studies, and Cytotoxic Activities of Metal(II) Schiff Base Complexes
Hatice Gamze Soğukömeroğulları, Bülent Dede, Dicle Sahin, et al.
Applied Organometallic Chemistry (2025) Vol. 39, Iss. 3
Open Access
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion
Tianyi Wang, Qilong Wu, Yun Han, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Open Access
Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning
Jiexin Shi, Xiaohong Zhang, Venkata Rohit Punyapu, et al.
The Journal of Chemical Physics (2025) Vol. 162, Iss. 8
Closed Access
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