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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:

tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes
David Balcells, Bastian Bjerkem Skjelstad
Journal of Chemical Information and Modeling (2020) Vol. 60, Iss. 12, pp. 6135-6146
Open Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew Rosen, Shaelyn Iyer, Debmalya Ray, et al.
Matter (2021) Vol. 4, Iss. 5, pp. 1578-1597
Open Access | Times Cited: 327
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Aditya Nandy, Chenru Duan, Michael G. Taylor, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9927-10000
Closed Access | Times Cited: 218
Ab Initio Machine Learning in Chemical Compound Space
Bing Huang, O. Anatole von Lilienfeld
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 10001-10036
Open Access | Times Cited: 127
QMugs, quantum mechanical properties of drug-like molecules
Clemens Isert, Kenneth Atz, José Jiménez-Luna, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 85
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
Bohayra Mortazavi
Advanced Energy Materials (2024)
Open Access | Times Cited: 21
Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Chemical Reviews (2024) Vol. 124, Iss. 24, pp. 13681-13714
Closed Access | Times Cited: 17
The Rise of Neural Networks for Materials and Chemical Dynamics
Maksim Kulichenko, Justin S. Smith, Benjamin Nebgen, et al.
The Journal of Physical Chemistry Letters (2021) Vol. 12, Iss. 26, pp. 6227-6243
Open Access | Times Cited: 76
Machine learning and big data provide crucial insight for future biomaterials discovery and research
Jacob Kerner, Alan B. Dogan, Horst A. von Recum
Acta Biomaterialia (2021) Vol. 130, pp. 54-65
Closed Access | Times Cited: 64
%VBur index and steric maps: from predictive catalysis to machine learning
Sílvia Escayola, Naeimeh Bahri‐Laleh, Albert Poater
Chemical Society Reviews (2023) Vol. 53, Iss. 2, pp. 853-882
Open Access | Times Cited: 35
Architector for high-throughput cross-periodic table 3D complex building
Michael G. Taylor, Daniel J. Burrill, Jan Janßen, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 28
Deep learning metal complex properties with natural quantum graphs
Hannes Kneiding, Ruslan Lukin, Lucas Lang, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 618-633
Open Access | Times Cited: 24
Directional multiobjective optimization of metal complexes at the billion-system scale
Hannes Kneiding, Ainara Nova, David Balcells
Nature Computational Science (2024) Vol. 4, Iss. 4, pp. 263-273
Open Access | Times Cited: 14
Modeling Zinc Complexes Using Neural Networks
H.-Q. Jin, Kenneth M. Merz
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 3140-3148
Open Access | Times Cited: 9
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Aditya Nandy, Chenru Duan, Heather J. Kulik
Current Opinion in Chemical Engineering (2021) Vol. 36, pp. 100778-100778
Open Access | Times Cited: 49
High-throughput virtual screening for organic electronics: a comparative study of alternative strategies
Ömer H. Omar, Marcos del Cueto, Tahereh Nematiaram, et al.
Journal of Materials Chemistry C (2021) Vol. 9, Iss. 39, pp. 13557-13583
Open Access | Times Cited: 44
Machine Learning for the Discovery, Design, and Engineering of Materials
Chenru Duan, Aditya Nandy, Heather J. Kulik
Annual Review of Chemical and Biomolecular Engineering (2022) Vol. 13, Iss. 1, pp. 405-429
Open Access | Times Cited: 38
Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design
Wataru Matsuoka, Yu Harabuchi, Satoshi Maeda
ACS Catalysis (2023) Vol. 13, Iss. 8, pp. 5697-5711
Open Access | Times Cited: 19
Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network
Conor D. Rankine, Thomas J. Penfold
The Journal of Chemical Physics (2022) Vol. 156, Iss. 16
Open Access | Times Cited: 28
Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes
Aditya Nandy, Michael G. Taylor, Heather J. Kulik
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 25, pp. 5798-5804
Closed Access | Times Cited: 16
Hybrid DFT Geometries and Properties for 17k Lanthanoid Complexes─The LnQM Data Set
Christian Hölzer, Igor Gordiy, Stefan Grimme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 825-836
Closed Access | Times Cited: 6
Computational Organometallic Catalysis: Where We Are, Where We Are Going
Agustı́ Lledós
European Journal of Inorganic Chemistry (2021) Vol. 2021, Iss. 26, pp. 2547-2555
Open Access | Times Cited: 30
cell2mol: encoding chemistry to interpret crystallographic data
Sergi Vela, Rubén Laplaza, Yuri Cho, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 21
Efficient calculation of protein–ligand binding free energy using GFN methods: the power of the cluster model
Yuan-Qiang Chen, Yan-Jing Sheng, Yu‐qiang Ma, et al.
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 23, pp. 14339-14347
Closed Access | Times Cited: 20
QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets
Rebecca M. Neeser, Clemens Isert, Thijs Stuyver, et al.
Chemical Data Collections (2023) Vol. 46, pp. 101040-101040
Closed Access | Times Cited: 10
Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach
Eduardo Alberto Aguilar Bejarano, Viraj Deorukhkar, Simon Woodward
Molecules (2025) Vol. 30, Iss. 2, pp. 355-355
Open Access
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