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:
Neural network potentials for chemistry: concepts, applications and prospects
Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, et al.
Digital Discovery (2022) Vol. 2, Iss. 1, pp. 28-58
Open Access | Times Cited: 61
Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, et al.
Digital Discovery (2022) Vol. 2, Iss. 1, pp. 28-58
Open Access | Times Cited: 61
Showing 1-25 of 61 citing articles:
The Challenges of Machine Learning: A Critical Review
Enrico Barbierato, Alice Gatti
Electronics (2024) Vol. 13, Iss. 2, pp. 416-416
Open Access | Times Cited: 28
Enrico Barbierato, Alice Gatti
Electronics (2024) Vol. 13, Iss. 2, pp. 416-416
Open Access | Times Cited: 28
Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
Amir Omranpour, Pablo Montero de Hijes, Jörg Behler, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 17
Open Access | Times Cited: 16
Amir Omranpour, Pablo Montero de Hijes, Jörg Behler, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 17
Open Access | Times Cited: 16
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: 20
Marco Eckhoff, Markus Reiher
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 12, pp. 3509-3525
Open Access | Times Cited: 20
Machine Learning of Reactive Potentials
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, et al.
Annual Review of Physical Chemistry (2024) Vol. 75, Iss. 1, pp. 371-395
Closed Access | Times Cited: 11
Yinuo Yang, Shuhao Zhang, Kavindri Ranasinghe, et al.
Annual Review of Physical Chemistry (2024) Vol. 75, Iss. 1, pp. 371-395
Closed Access | Times Cited: 11
Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications
Claudia Arellano, Joseph Govan
Agronomy (2024) Vol. 14, Iss. 2, pp. 341-341
Open Access | Times Cited: 9
Claudia Arellano, Joseph Govan
Agronomy (2024) Vol. 14, Iss. 2, pp. 341-341
Open Access | Times Cited: 9
An overview about neural networks potentials in molecular dynamics simulation
Raidel Martin‐Barrios, Edisel Navas‐Conyedo, Xuyi Zhang, et al.
International Journal of Quantum Chemistry (2024) Vol. 124, Iss. 11
Closed Access | Times Cited: 7
Raidel Martin‐Barrios, Edisel Navas‐Conyedo, Xuyi Zhang, et al.
International Journal of Quantum Chemistry (2024) Vol. 124, Iss. 11
Closed Access | Times Cited: 7
ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies
Seyedeh Fatemeh Alavi, Yuxinxin Chen, Yi-Fan Hou, et al.
The Journal of Physical Chemistry Letters (2025), pp. 483-493
Closed Access
Seyedeh Fatemeh Alavi, Yuxinxin Chen, Yi-Fan Hou, et al.
The Journal of Physical Chemistry Letters (2025), pp. 483-493
Closed Access
Machine learning in electrocatalysis - living up to the hype?
Árni Björn Höskuldsson
Current Opinion in Electrochemistry (2025), pp. 101649-101649
Closed Access
Árni Björn Höskuldsson
Current Opinion in Electrochemistry (2025), pp. 101649-101649
Closed Access
Automated and Efficient Sampling of Chemical Reaction Space
Minhyeok Lee, Umit Volkan Ucak, Jinyoung Jeong, et al.
Advanced Science (2025)
Open Access
Minhyeok Lee, Umit Volkan Ucak, Jinyoung Jeong, et al.
Advanced Science (2025)
Open Access
Machine Learning Potentials for Heterogeneous Catalysis
Amir Omranpour, Jan Elsner, K. Nikolas Lausch, et al.
ACS Catalysis (2025), pp. 1616-1634
Open Access
Amir Omranpour, Jan Elsner, K. Nikolas Lausch, et al.
ACS Catalysis (2025), pp. 1616-1634
Open Access
New Algorithms to Generate Permutationally Invariant Polynomials and Fundamental Invariants for Potential Energy Surface Fitting
Yiping Hao, Xiaoxiao Lu, Bina Fu, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access
Yiping Hao, Xiaoxiao Lu, Bina Fu, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access
Enantioselective Synthesis of Pillar[5]arenes by Dynamic Kinetic Resolution via Pd-Catalyzed Suzuki Cross-Coupling
A. Konter, Jaume Rostoll‐Berenguer, Céline Besnard, et al.
ACS Catalysis (2025), pp. 2607-2619
Closed Access
A. Konter, Jaume Rostoll‐Berenguer, Céline Besnard, et al.
ACS Catalysis (2025), pp. 2607-2619
Closed Access
Gaussian Process Approach to Constructing Transferable Force Fields for Thiolate-Protected Gold Nanoclusters
Yuchen Wang, D. Sulalith N. D. Samarasinghe, Hao Deng, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Yuchen Wang, D. Sulalith N. D. Samarasinghe, Hao Deng, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Carbon Dioxide Sensing Based on Off-Axis Integrated Cavity Absorption Spectroscopy Combined with the Informer and Multilayer Perceptron Models
Kehao Zhang, Tao Wu, Linlin Shen, et al.
Analytical Chemistry (2025)
Closed Access
Kehao Zhang, Tao Wu, Linlin Shen, et al.
Analytical Chemistry (2025)
Closed Access
Study of methyl phosphate by molecular dynamics simulations based on first principles and on machine-learning force fields
Vincenzo Turco Liveri, Sandro L. Fornili
Journal of Molecular Liquids (2025), pp. 127062-127062
Closed Access
Vincenzo Turco Liveri, Sandro L. Fornili
Journal of Molecular Liquids (2025), pp. 127062-127062
Closed Access
Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design
Alexander Zlobin, Valentina Maslova, Julia Beliaeva, et al.
Journal of Chemical Information and Modeling (2025)
Open Access
Alexander Zlobin, Valentina Maslova, Julia Beliaeva, et al.
Journal of Chemical Information and Modeling (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
Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning
Guzhong Chen, Zhen Song, Zhiwen Qi, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 591-601
Open Access | Times Cited: 19
Guzhong Chen, Zhen Song, Zhiwen Qi, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 591-601
Open Access | Times Cited: 19
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: 17
Kaiwei Wan, Jianxin He, Xinghua Shi
Advanced Materials (2023) Vol. 36, Iss. 22
Closed Access | Times Cited: 17
Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential
Sara Tkaczyk, Johannes Karwounopoulos, Andreas Schöller, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 7, pp. 2719-2728
Closed Access | Times Cited: 5
Sara Tkaczyk, Johannes Karwounopoulos, Andreas Schöller, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 7, pp. 2719-2728
Closed Access | Times Cited: 5
In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back
Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, et al.
Advanced Materials (2024) Vol. 36, Iss. 30
Closed Access | Times Cited: 5
Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, et al.
Advanced Materials (2024) Vol. 36, Iss. 30
Closed Access | Times Cited: 5
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
Rolf David, Miguel de la Puente, Axel Gomez, et al.
Digital Discovery (2024)
Open Access | Times Cited: 5
Rolf David, Miguel de la Puente, Axel Gomez, et al.
Digital Discovery (2024)
Open Access | Times Cited: 5
Inelastic N$$_2$$+H$$_2$$ collisions and quantum-classical rate coefficients: large datasets and machine learning predictions
Qizhen Hong, Loriano Storchi, Massimiliano Bartolomei, et al.
The European Physical Journal D (2023) Vol. 77, Iss. 7
Open Access | Times Cited: 12
Qizhen Hong, Loriano Storchi, Massimiliano Bartolomei, et al.
The European Physical Journal D (2023) Vol. 77, Iss. 7
Open Access | Times Cited: 12
Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at CCSD(T) quality
Silvan Käser, Markus Meuwly
The Journal of Chemical Physics (2023) Vol. 158, Iss. 21
Open Access | Times Cited: 11
Silvan Käser, Markus Meuwly
The Journal of Chemical Physics (2023) Vol. 158, Iss. 21
Open Access | Times Cited: 11
Challenges and Opportunities of Molecular Simulations for Negative Gas Adsorption
Jack D. Evans, François‐Xavier Coudert
Accounts of Materials Research (2024) Vol. 5, Iss. 5, pp. 640-647
Open Access | Times Cited: 3
Jack D. Evans, François‐Xavier Coudert
Accounts of Materials Research (2024) Vol. 5, Iss. 5, pp. 640-647
Open Access | Times Cited: 3
