Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence
Chuanjun Zhan, Zhenxue Dai, Shangxian Yin, et al.
Water Research (2024) Vol. 257, pp. 121679-121679
Closed Access | Times Cited: 33
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 18
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Kamal Choudhary, Daniel Wines, Kangming Li, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 14
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 14
Roadmap on data-centric materials science
Sebastian Bauer, Peter Benner, Tristan Bereau, et al.
Modelling and Simulation in Materials Science and Engineering (2024) Vol. 32, Iss. 6, pp. 063301-063301
Open Access | Times Cited: 8
Machine Learning Interatomic Potentials for Catalysis
Deqi Tang, Rangsiman Ketkaew, Sandra Luber
Chemistry - A European Journal (2024) Vol. 30, Iss. 60
Open Access | Times Cited: 8
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, X. L. Lei, et al.
Communications Materials (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 1
Optimizing casting process using a combination of small data machine learning and phase-field simulations
Xiaolong Pei, Jiaqi Pei, Hua Hou, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 21, pp. 12412-12422
Open Access | Times Cited: 5
Few-Shot Learning of Initial Coulombic Efficiency Using Adaptive Combination Kernel Deep Gaussian Process Regression
Hai Guo, Hongcheng Zhang, Xiaofeng Lv, et al.
(2025)
Closed Access
Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty
Qinghua Wei, Yuanhao Wang, Yang Guo, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Simultaneous prediction of 10m and 100m wind speeds using a model inspired by brain emotional learning
Alireza Hakimi, Parvin Ghafarian
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Exploring Insecticidal Molecules with Random Forest: Toward High Insecticidal Activity and Low Bee Toxicity
Wei Guo, Xiangmin Song, Yong‐Chao Gao, et al.
Journal of Agricultural and Food Chemistry (2025) Vol. 73, Iss. 9, pp. 5573-5584
Closed Access
Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites
Wei Deng, Lijun Liu, Xiaohang Li, et al.
Advanced Materials (2025)
Closed Access
A significant enhancement in thermal conductivity of plastic crystals under compressive strain by deep potential molecular dynamics
Yangjun Qin, Zhicheng Zong, Junwei Che, et al.
Applied Physics Letters (2025) Vol. 126, Iss. 10
Closed Access
Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management
Linjun Lu, Alix Marie d’Avigneau, Yuandong Pan, et al.
Automation in Construction (2025) Vol. 174, pp. 106134-106134
Open Access
Realistic material property prediction using domain adaptation based machine learning
Jeffrey Hu, David Liu, Nihang Fu, et al.
Digital Discovery (2023) Vol. 3, Iss. 2, pp. 300-312
Open Access | Times Cited: 12
Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles
Ravithree D. Senanayake, Clyde A. Daly, Rigoberto Hernandez
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 14, pp. 2857-2870
Closed Access | Times Cited: 4
Machine learning-assisted wood materials: Applications and future prospects
Yuqi Feng, Saad Mekhilef, David Hui, et al.
Extreme Mechanics Letters (2024) Vol. 71, pp. 102209-102209
Closed Access | Times Cited: 4
COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets
Zhen Cao, Laura Falivene, Albert Poater, et al.
Cell Reports Physical Science (2024), pp. 102348-102348
Open Access | Times Cited: 3
Key requirements for advancing machine learning approaches in single entity electrochemistry
Viacheslav Shkirskiy, Frédéric Kanoufi
Current Opinion in Electrochemistry (2024) Vol. 46, pp. 101526-101526
Open Access | Times Cited: 2
Interpretable Machine Learning for Investigating the Molecular Mechanisms Governing the Transparency of Colorless Transparent Polyimide for OLED Cover Windows
Songyang Zhang, Xiaojie He, Peng Xiao, et al.
Advanced Functional Materials (2024)
Closed Access | Times Cited: 2
MD-HIT: Machine learning for material property prediction with dataset redundancy control
Qin Li, Nihang Fu, Sadman Sadeed Omee, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 2
Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials
Jaesun Kim, Jisu Kim, Jaehoon Kim, et al.
Journal of the American Chemical Society (2024)
Closed Access | Times Cited: 2
Page 1 - Next Page

Scroll to top