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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:

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning
Ganesh Sivaraman, Nicholas E. Jackson
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 2, pp. 1129-1141
Open Access | Times Cited: 21

Showing 21 citing articles:

Bottom-up Coarse-Graining: Principles and Perspectives
Jaehyeok Jin, Alexander J. Pak, Aleksander E. P. Durumeric, et al.
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 10, pp. 5759-5791
Open Access | Times Cited: 171
Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
W. G. Noid
The Journal of Physical Chemistry B (2023) Vol. 127, Iss. 19, pp. 4174-4207
Open Access | Times Cited: 80
Data-driven approaches for structure-property relationships in polymer science for prediction and understanding
Yoshifumi Amamoto
Polymer Journal (2022) Vol. 54, Iss. 8, pp. 957-967
Closed Access | Times Cited: 34
Ensuring thermodynamic consistency with invertible coarse-graining
Shriram Chennakesavalu, David J. Toomer, Grant M. Rotskoff
The Journal of Chemical Physics (2023) Vol. 158, Iss. 12
Open Access | Times Cited: 22
Simulation of polymeric mixed ionic and electronic conductors with a combined classical and quantum mechanical model
Alessandro Landi, Maryam Reisjalali, Joshua D. Elliott, et al.
Journal of Materials Chemistry C (2023) Vol. 11, Iss. 24, pp. 8062-8073
Open Access | Times Cited: 17
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
André Nicolle, Sili Deng, Matthias Ihme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 597-620
Closed Access | Times Cited: 7
Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers
Eleonora Ricci, Niki Vergadou
The Journal of Physical Chemistry B (2023) Vol. 127, Iss. 11, pp. 2302-2322
Closed Access | Times Cited: 16
Bringing Quantum Mechanics to Coarse-Grained Soft Materials Modeling
Chun-I Wang, Nicholas E. Jackson
Chemistry of Materials (2023) Vol. 35, Iss. 4, pp. 1470-1486
Closed Access | Times Cited: 15
Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions
Riccardo Alessandri, Juan Pablo
Macromolecules (2023) Vol. 56, Iss. 10, pp. 3574-3584
Open Access | Times Cited: 14
Bypassing backmapping: Coarse-grained electronic property distributions using heteroscedastic Gaussian processes
J. Charlie Maier, Nicholas E. Jackson
The Journal of Chemical Physics (2022) Vol. 157, Iss. 17
Closed Access | Times Cited: 20
Gaussian Process Regression Models for Predicting Atomic Energies and Multipole Moments
Matthew J. Burn, Paul L. A. Popelier
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 4, pp. 1370-1380
Open Access | Times Cited: 11
Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining
Chun-I Wang, J. Charlie Maier, Nicholas E. Jackson
Chemical Science (2024) Vol. 15, Iss. 22, pp. 8390-8403
Open Access | Times Cited: 4
Synthetic data enable experiments in atomistic machine learning
John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 651-662
Open Access | Times Cited: 10
Deep Kernel learning for reaction outcome prediction and optimization
Sukriti Singh, José Miguel Herńandez-Lobato
Communications Chemistry (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Ultrafast Electronic Coupling Estimators: Neural Networks versus Physics-Based Approaches
Roohollah Hafizi, Jan Elsner, Jochen Blumberger
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 13, pp. 4232-4242
Open Access | Times Cited: 8
Identifying Coarse-Grained Representations for Electronic Predictions
Chun-I Wang, J. Charlie Maier, Nicholas E. Jackson
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 15, pp. 4982-4990
Closed Access | Times Cited: 4
Rigorous Progress in Coarse-Graining
W. G. Noid, Ryan J. Szukalo, Katherine M. Kidder, et al.
Annual Review of Physical Chemistry (2024) Vol. 75, Iss. 1, pp. 21-45
Closed Access | Times Cited: 1
Machine-Learning-Assisted Acceleration on High-Symmetry Materials Search: Space Group Predictions from Band Structures
Bin Xi, Kin Fai Tse, Tsz-Fung Kok, et al.
The Journal of Physical Chemistry C (2022) Vol. 126, Iss. 29, pp. 12264-12273
Closed Access | Times Cited: 5
Calibration of uncertainty in the active learning of machine learning force fields
Adam Thomas-Mitchell, Glenn Hawe, Paul L. A. Popelier
Machine Learning Science and Technology (2023) Vol. 4, Iss. 4, pp. 045034-045034
Open Access | Times Cited: 2
Chemically Transferable Electronic Coarse Graining for Polythiophenes
Zheng Yu, Nicholas E. Jackson
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 20, pp. 9116-9127
Closed Access
MVCAL: Multi View Clustering for Active Learning
Y. Z. Fan, Biao Jiang, Di Chen, et al.
Communications in computer and information science (2023), pp. 506-517
Closed Access
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