Open Access Helper

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:

Insights into lithium manganese oxide–water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
The Journal of Chemical Physics (2021) Vol. 155, Iss. 24
Open Access | Times Cited: 39

Showing 26-50 of 39 citing articles:

CoRe Optimizer: An All-in-One Solution for Machine Learning
Marco Eckhoff, Markus Reiher
Machine Learning Science and Technology (2024)
Open Access | Times Cited: 2
Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultralong Simulation in Lithium-Metal Batteries
Longkun Xu, Wei Shao, Haishun Jin, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 50, pp. 24106-24117
Closed Access | Times Cited: 5
Chemical design by artificial intelligence
Daniel H. Ess, Kim E. Jelfs, Heather J. Kulik
The Journal of Chemical Physics (2022) Vol. 157, Iss. 12
Open Access | Times Cited: 6
Tutorial: How to Train a Neural Network Potential
Alea Miako Tokita, Jörg Behler
arXiv (Cornell University) (2023)
Open Access | Times Cited: 2
Combining NMR and molecular dynamics simulations for revealing the alkali-ion transport in solid-state battery materials
Min Lin, Riqiang Fu, Yuxuan Xiang, et al.
Current Opinion in Electrochemistry (2022) Vol. 35, pp. 101048-101048
Closed Access | Times Cited: 3
Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultra-long Simulation in Lithium Metal Batteries
Longkun Xu, Wei Shao, Haishun Jin, et al.
(2023)
Open Access | Times Cited: 1
Gaussian Attractive Potential for Carboxylate/Cobalt Surface Interactions
Xiaojing WU, Stephan N. Steinmann, Carine Michel
(2023)
Open Access | Times Cited: 1
Gaussian Attractive Potential for Carboxylate/Cobalt Surface Interactions
Xiaojing WU, Stephan N. Steinmann, Carine Michel
(2023)
Open Access | Times Cited: 1
Gaussian attractive potential for carboxylate/cobalt surface interactions
Xiaojing Wu, Stephan N. Steinmann, Carine Michel
The Journal of Chemical Physics (2023) Vol. 159, Iss. 16
Closed Access | Times Cited: 1
Development of Machine Learning Atomistic Potential for Molecular Simulation of Hematite–Water Interfaces
Mozhdeh Shiranirad, Niall J. English
Crystals (2024) Vol. 14, Iss. 11, pp. 930-930
Open Access
Lifelong Machine Learning Potentials
Marco Eckhoff, Markus Reiher
arXiv (Cornell University) (2023)
Open Access
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance
Vidushi Sharma, Maxwell J. Giammona, Dmitry Yu. Zubarev, et al.
arXiv (Cornell University) (2023)
Open Access
CoRe Optimizer: An All-in-One Solution for Machine Learning
Marco Eckhoff, Markus Reiher
arXiv (Cornell University) (2023)
Open Access
Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultra-long Simulation in Lithium Metal Batteries
Longkun Xu, Wei Shao, Haishun Jin, et al.
(2023)
Open Access
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