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:
A Euclidean transformer for fast and stable machine learned force fields
J. Thorben Frank, Oliver T. Unke, Klaus‐Robert Müller, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
J. Thorben Frank, Oliver T. Unke, Klaus‐Robert Müller, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Showing 9 citing articles:
Towards symbolic XAI – explanation through human understandable logical relationships between features
Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, et al.
Information Fusion (2025), pp. 102923-102923
Open Access | Times Cited: 1
Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, et al.
Information Fusion (2025), pp. 102923-102923
Open Access | Times Cited: 1
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer, et al.
Journal of Chemical Theory and Computation (2025)
Open Access
Malte Esders, Thomas Schnake, Jonas Lederer, et al.
Journal of Chemical Theory and Computation (2025)
Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Chemical Science (2025)
Open Access
Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Chemical Science (2025)
Open Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, et al.
Chemical Science (2025)
Open Access
Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, et al.
Chemical Science (2025)
Open Access
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 3
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 3
Efficient prediction of potential energy surface and physical properties with Kolmogorov-Arnold Networks
Rui Wang, Hongyu Yu, Yang Zhong, et al.
Journal of Materials Informatics (2024)
Open Access
Rui Wang, Hongyu Yu, Yang Zhong, et al.
Journal of Materials Informatics (2024)
Open Access
Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials
Christopher D. Williams, Jas Kalayan, Neil A. Burton, et al.
Chemical Science (2024) Vol. 15, Iss. 32, pp. 12780-12795
Open Access
Christopher D. Williams, Jas Kalayan, Neil A. Burton, et al.
Chemical Science (2024) Vol. 15, Iss. 32, pp. 12780-12795
Open Access
Complete and Efficient Covariants for Three-Dimensional Point Configurations with Application to Learning Molecular Quantum Properties
Hartmut Maennel, Oliver T. Unke, Klaus‐Robert Mueller
The Journal of Physical Chemistry Letters (2024), pp. 12513-12519
Open Access
Hartmut Maennel, Oliver T. Unke, Klaus‐Robert Mueller
The Journal of Physical Chemistry Letters (2024), pp. 12513-12519
Open Access
Mechanical Properties of Nanoporous Graphenes: Transferability of Graph Machine‐Learned Force Fields Compared to Local and Reactive Potentials
Adil Kabylda, Bohayra Mortazavi, Xiaoying Zhuang, et al.
Advanced Functional Materials (2024)
Open Access
Adil Kabylda, Bohayra Mortazavi, Xiaoying Zhuang, et al.
Advanced Functional Materials (2024)
Open Access
