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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:

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review
Kaiwei Wan, Jianxin He, Xinghua Shi
Advanced Materials (2023) Vol. 36, Iss. 22
Closed Access | Times Cited: 24

Showing 24 citing articles:

Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 17
Machine learning-assisted carbon dots synthesis and analysis: state of the art and future directions
Fanyong Yan, Ruixue Bai, Juanru Huang, et al.
TrAC Trends in Analytical Chemistry (2025), pp. 118141-118141
Closed Access | Times Cited: 1
Machine Learning Interatomic Potentials for Catalysis
Deqi Tang, Rangsiman Ketkaew, Sandra Luber
Chemistry - A European Journal (2024) Vol. 30, Iss. 60
Open Access | Times Cited: 8
Unified deep learning network for enhanced accuracy in predicting thermal conductivity of bilayer graphene, hexagonal boron nitride, and their heterostructures
Rongkun Chen, Yu Tian, Jiayi Cao, et al.
Journal of Applied Physics (2024) Vol. 135, Iss. 14
Open Access | Times Cited: 6
Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis
Xiran Cheng, Chenyu Wu, Jiayan Xu, et al.
Precision Chemistry (2024) Vol. 2, Iss. 11, pp. 570-586
Open Access | Times Cited: 6
Improving Molecular‐Dynamics Simulations for Solid–Liquid Interfaces with Machine‐Learning Interatomic Potentials
Pengfei Hou, Yumiao Tian, Xing Meng
Chemistry - A European Journal (2024) Vol. 30, Iss. 49
Closed Access | Times Cited: 5
Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives
Panpan Zhou, Qianwen Zhou, Xuezhang Xiao, et al.
Advanced Materials (2024)
Closed Access | Times Cited: 4
DFT Insights into the Mechanical Properties of NMs
Mohammad Aminul Islam, Nayem Hossain, Zahid Ahsan, et al.
Results in Surfaces and Interfaces (2025) Vol. 18, pp. 100417-100417
Open Access
Development of Machine Learning Potentials for Ce-Ti and Ce-Ta Binary Systems and Studies of the Liquid-Solid Interfaces
Hongjian Chen, Jianfeng Cai, Yunhan Zhang, et al.
Corrosion Science (2025), pp. 112766-112766
Closed Access
A practical guide to machine learning interatomic potentials – Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian, et al.
Current Opinion in Solid State and Materials Science (2025) Vol. 35, pp. 101214-101214
Closed Access
Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites
Wei Deng, Lijun Liu, Xiaohang Li, et al.
Advanced Materials (2025)
Closed Access
Applications of Molecular Dynamics in Nanomaterial Design and Characterization - A Review
Md Aminul Islam, Sayma Rahman, Juhi Jannat Mim, et al.
Chemical Engineering Journal Advances (2025), pp. 100731-100731
Open Access
Decoding the Thermal Conductivity of Ionic Covalent Organic Frameworks: Optical Phonons as Key Determinants Revealed by Neuroevolution Potential
Ke Li, Hao Ma
Materials Today Physics (2025), pp. 101724-101724
Closed Access
The evolution of machine learning potentials for molecules, reactions and materials
Junfan Xia, Yaolong Zhang, Bin Jiang
Chemical Society Reviews (2025)
Open Access
Computational Insights into the structural, electronic, mechanical, and optical properties of Cu, Ge, and Au-doped CsTiO3 for Optoelectronic Applications
Tehreem Fatima, Abdul Waheed Anwar, M. Basit Shakir, et al.
Computational Condensed Matter (2025), pp. e01056-e01056
Closed Access
Applications of machine learning in surfaces and interfaces
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
Progress in Computational Methods and Mechanistic Insights on the Growth of Carbon Nanotubes
Linzheng Wang, Nicolas Tricard, Zi Chen, et al.
Nanoscale (2025)
Open Access
Applications of density functional theory and machine learning in nanomaterials: A review
Nangamso Nathaniel Nyangiwe
Next Materials (2025) Vol. 8, pp. 100683-100683
Closed Access
Predicting nanoscale stress-strain curves: A Gaussian processes within a Bayesian framework
Ahmad Altarabsheh, Ibrahim Altarabsheh, Xiang Chen
International Journal of Solids and Structures (2025), pp. 113438-113438
Closed Access
Understanding Surface/Interface‐Induced Chemical and Physical Properties at Atomic Level by First Principles Investigations
Jingyu Yang, Jinbo Pan, Shixuan Du
Wiley Interdisciplinary Reviews Computational Molecular Science (2025) Vol. 15, Iss. 3
Closed Access
Leveraging generative models with periodicity-aware, invertible and invariant representations for crystalline materials design
Zhilong Wang, Fengqi You
Nature Computational Science (2025)
Closed Access
Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion
Xiaoya Chang, Di Zhang, Qingzhao Chu, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 15, pp. 6813-6825
Closed Access | Times Cited: 3
Zero-valent iron-based materials for enhanced reductive removal of contaminants: From the trial-and-error synthesis to rational design
Yinghao Shi, Jiaming Guo, Feilong Gao, et al.
Applied Catalysis B Environment and Energy (2024), pp. 124901-124901
Closed Access | Times Cited: 2
Application of Nanoencapsulation Technology in Agriculture for Effective and Sustainable Weed Management: A Critical Review
R. Sabarivasan, P. Murali Arthanari
Communications in Soil Science and Plant Analysis (2024) Vol. 56, Iss. 2, pp. 277-291
Closed Access | Times Cited: 1
Biomimetic fusion: Platyper's dual vision for predicting protein–surface interactions
Chuhang Hong, Xiaopei Wu, Jian Huang, et al.
Materials Horizons (2024) Vol. 11, Iss. 15, pp. 3528-3538
Closed Access
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