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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:

Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet
Pierre-Paul De Breuck, Matthew L. Evans, Gian‐Marco Rignanese
Journal of Physics Condensed Matter (2021) Vol. 33, Iss. 40, pp. 404002-404002
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Leveraging large language models for predictive chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 2, pp. 161-169
Open Access | Times Cited: 112
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian DeCost, Kamal Choudhary, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 57
Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings
Shufeng Kong, Francesco Ricci, Dan Guevarra, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 56
A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
Cameron J. Hargreaves, Michael W. Gaultois, Luke M. Daniels, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 35
Is GPT-3 all you need for low-data discovery in chemistry?
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
(2023)
Open Access | Times Cited: 25
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
Son Gyo Jung, Guwon Jung, Jacqueline M. Cole
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 4, pp. 1187-1200
Open Access | Times Cited: 13
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, J. Bergsma, Andrius Merkys, et al.
Digital Discovery (2024) Vol. 3, Iss. 8, pp. 1509-1533
Open Access | Times Cited: 10
Uncertainty quantification by direct propagation of shallow ensembles
Matthias Kellner, Michele Ceriotti
Machine Learning Science and Technology (2024) Vol. 5, Iss. 3, pp. 035006-035006
Open Access | Times Cited: 9
Leveraging Large Language Models for Predictive Chemistry
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega‐Guerrero, et al.
(2023)
Open Access | Times Cited: 18
Combination of ab initio descriptors and machine learning approach for the prediction of the plasticity mechanisms in β-metastable Ti alloys
M. Coffigniez, P.-P. De Breuck, Laurine Choisez, et al.
Materials & Design (2024) Vol. 239, pp. 112801-112801
Open Access | Times Cited: 7
Benchmarking active learning strategies for materials optimization and discovery
Alex Jinpeng Wang, Haotong Liang, Austin McDannald, et al.
Oxford Open Materials Science (2022) Vol. 2, Iss. 1
Open Access | Times Cited: 25
A Quantum-Chemical Bonding Database for Solid-State Materials
Aakash Ashok Naik, Christina Ertural, Nidal Dhamrait, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 11
Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery
Achintha Ihalage, Yang Hao
Advanced Science (2022) Vol. 9, Iss. 18
Open Access | Times Cited: 17
Gradient boosted and statistical feature selection workflow for materials property predictions
Son Gyo Jung, Guwon Jung, Jacqueline M. Cole
The Journal of Chemical Physics (2023) Vol. 159, Iss. 19
Open Access | Times Cited: 10
CrysToGraph: A Comprehensive Predictive Model for Crystal Material Properties and the Benchmark
Hongyi Wang, Ji Ung Sun, Jinzhe Liang, et al.
Battery energy (2025)
Open Access
Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks
Vishu Gupta, Youjia Li, Alec Peltekian, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 3
Machine Learning to Promote Efficient Screening of Low‐Contact Electrode for 2D Semiconductor Transistor Under Limited Data
Penghui Li, Linpeng Dong, Chong Li, et al.
Advanced Materials (2024) Vol. 36, Iss. 26
Closed Access | Times Cited: 3
Evaluation of principal features for predicting bulk and shear modulus of inorganic solids with machine learning
Myeonghun Lee, Minseon Kim, Kyoungmin Min
Materials Today Communications (2022) Vol. 33, pp. 104208-104208
Closed Access | Times Cited: 12
Materials Science Optimization Benchmark Dataset for Multi-fidelity Hard-sphere Packing Simulations
Sterling G. Baird, Taylor D. Sparks
(2023)
Open Access | Times Cited: 4
Impact of data bias on machine learning for crystal compound synthesizability predictions
Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 4, pp. 040501-040501
Open Access | Times Cited: 1
Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery
Giovanni Trezza, Eliodoro Chiavazzo
Journal of Chemical Information and Modeling (2024)
Open Access | Times Cited: 1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
Hongwei Du, Jiamin Wang, Jian Hui, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 1
Materials science optimization benchmark dataset for multi-objective, multi-fidelity optimization of hard-sphere packing simulations
Sterling G. Baird, Ramsey Issa, Taylor D. Sparks
Data in Brief (2023) Vol. 50, pp. 109487-109487
Open Access | Times Cited: 3
Accurate experimental band gap predictions with multifidelity correction learning
Pierre-Paul De Breuck, Grégoire Heymans, Gian‐Marco Rignanese
Journal of Materials Informatics (2022) Vol. 2, Iss. 3, pp. 10-10
Open Access | Times Cited: 4
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
Hong Wang, Hongwei Du, Jian Hui, et al.
Research Square (Research Square) (2024)
Open Access
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