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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:

Recent advances and applications of machine learning in solid-state materials science
Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, et al.
npj Computational Materials (2019) Vol. 5, Iss. 1
Open Access | Times Cited: 1894

Showing 26-50 of 1894 citing articles:

Polymer informatics: Current status and critical next steps
Lihua Chen, Ghanshyam Pilania, Rohit Batra, et al.
Materials Science and Engineering R Reports (2020) Vol. 144, pp. 100595-100595
Open Access | Times Cited: 213
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Edward O. Pyzer‐Knapp, Jed W. Pitera, Peter Staar, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 205
Machine learning for quantum matter
Juan Carrasquilla
Advances in Physics X (2020) Vol. 5, Iss. 1, pp. 1797528-1797528
Open Access | Times Cited: 202
Performance-Based Screening of Porous Materials for Carbon Capture
Amir H. Farmahini, Shreenath Krishnamurthy, Daniel Friedrich, et al.
Chemical Reviews (2021) Vol. 121, Iss. 17, pp. 10666-10741
Open Access | Times Cited: 202
Benchmarking graph neural networks for materials chemistry
Victor Fung, Jiaxin Zhang, Eric Juarez, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 200
Generative Adversarial Networks for Crystal Structure Prediction
Sungwon Kim, Juhwan Noh, Geun Ho Gu, et al.
ACS Central Science (2020) Vol. 6, Iss. 8, pp. 1412-1420
Open Access | Times Cited: 195
Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion
Haijiao Lu, Julie Tournet, Kamran Dastafkan, et al.
Chemical Reviews (2021) Vol. 121, Iss. 17, pp. 10271-10366
Closed Access | Times Cited: 195
Emerging Multifunctional Single-Atom Catalysts/Nanozymes
Huabin Zhang, Xue Feng Lu, Zhi‐Peng Wu, et al.
ACS Central Science (2020) Vol. 6, Iss. 8, pp. 1288-1301
Open Access | Times Cited: 193
A critical examination of compound stability predictions from machine-learned formation energies
Christopher J. Bartel, Amalie Trewartha, Qi Wang, et al.
npj Computational Materials (2020) Vol. 6, Iss. 1
Open Access | Times Cited: 190
Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures
Bohayra Mortazavi, Evgeny V. Podryabinkin, Stephan Roche, et al.
Materials Horizons (2020) Vol. 7, Iss. 9, pp. 2359-2367
Open Access | Times Cited: 188
Machine learning in concrete science: applications, challenges, and best practices
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 188
Machine learning for high-entropy alloys: Progress, challenges and opportunities
Xianglin Liu, Jiaxin Zhang, Zongrui Pei
Progress in Materials Science (2022) Vol. 131, pp. 101018-101018
Open Access | Times Cited: 188
Solid state chemistry for developing better metal-ion batteries
Artem M. Abakumov, Stanislav S. Fedotov, Evgeny V. Antipov, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 187
Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials
Bohayra Mortazavi, Ivan S. Novikov, Evgeny V. Podryabinkin, et al.
Applied Materials Today (2020) Vol. 20, pp. 100685-100685
Open Access | Times Cited: 175
Machine learning in materials science: From explainable predictions to autonomous design
Ghanshyam Pilania
Computational Materials Science (2021) Vol. 193, pp. 110360-110360
Open Access | Times Cited: 175
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
Moe Elbadawi, Brais Muñiz Castro, Francesca K. H. Gavins, et al.
International Journal of Pharmaceutics (2020) Vol. 590, pp. 119837-119837
Open Access | Times Cited: 171
Nucleation and growth in solution synthesis of nanostructures – From fundamentals to advanced applications
Ke‐Jun Wu, Edmund C. M. Tse, Congxiao Shang, et al.
Progress in Materials Science (2021) Vol. 123, pp. 100821-100821
Closed Access | Times Cited: 171
Searching for high entropy alloys: A machine learning approach
Kevin Kaufmann, Kenneth S. Vecchio
Acta Materialia (2020) Vol. 198, pp. 178-222
Open Access | Times Cited: 159
Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments
Shijie Jiang, Yi Zheng, Chao Wang, et al.
Water Resources Research (2021) Vol. 58, Iss. 1
Closed Access | Times Cited: 158
Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys
Dongbo Dai, Tao Xu, Xiao Wei, et al.
Computational Materials Science (2020) Vol. 175, pp. 109618-109618
Closed Access | Times Cited: 156
Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation
Soo Young Lee, Seokyeong Byeon, Hyoung Seop Kim, et al.
Materials & Design (2020) Vol. 197, pp. 109260-109260
Open Access | Times Cited: 153
Explainable machine learning in materials science
Xiaoting Zhong, Brian Gallagher, Shusen Liu, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 152
High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane
Thanh Nhat Nguyen, Thuy Tran Phuong Nhat, Ken Takimoto, et al.
ACS Catalysis (2019) Vol. 10, Iss. 2, pp. 921-932
Closed Access | Times Cited: 151
Deep learning analysis on microscopic imaging in materials science
M. Ge, Fei Su, Zhicheng Zhao, et al.
Materials Today Nano (2020) Vol. 11, pp. 100087-100087
Closed Access | Times Cited: 151
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 151

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