Matthew Welborn

3.4k total citations · 2 hit papers
40 papers, 2.4k citations indexed

About

Matthew Welborn is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Matthew Welborn has authored 40 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Materials Chemistry, 14 papers in Electrical and Electronic Engineering and 12 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Matthew Welborn's work include Machine Learning in Materials Science (11 papers), Advanced Chemical Physics Studies (8 papers) and Spectroscopy and Quantum Chemical Studies (5 papers). Matthew Welborn is often cited by papers focused on Machine Learning in Materials Science (11 papers), Advanced Chemical Physics Studies (8 papers) and Spectroscopy and Quantum Chemical Studies (5 papers). Matthew Welborn collaborates with scholars based in United States, United Kingdom and Iceland. Matthew Welborn's co-authors include Troy Van Voorhis, Thomas F. Miller, Lixue Cheng, Marc A. Baldo, Vladimir Bulović, Mengfei Wu, Nadav Geva, Daniel N. Congreve, Mark W. B. Wilson and Moungi G. Bawendi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Matthew Welborn

40 papers receiving 2.3k citations

Hit Papers

Solid-state infrared-to-visible upconversion sensitized b... 2014 2026 2018 2022 2015 2014 100 200 300 400

Peers

Matthew Welborn
Matthew Welborn
Citations per year, relative to Matthew Welborn Matthew Welborn (= 1×) peers Luca M. Ghiringhelli

Countries citing papers authored by Matthew Welborn

Since Specialization
Citations

This map shows the geographic impact of Matthew Welborn's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Welborn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Welborn more than expected).

Fields of papers citing papers by Matthew Welborn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Welborn. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Welborn. The network helps show where Matthew Welborn may publish in the future.

Co-authorship network of co-authors of Matthew Welborn

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Welborn. A scholar is included among the top collaborators of Matthew Welborn based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew Welborn. Matthew Welborn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cho, Minsik, Oskar Weser, Hong‐Zhou Ye, et al.. (2025). QuEmb: A Toolbox for Bootstrap Embedding Calculations of Molecular and Periodic Systems. The Journal of Physical Chemistry A. 129(28). 6538–6551. 2 indexed citations
2.
Li, Chaoyang, Yuxia Li, Xujia Zhang, et al.. (2023). A single‐cell atlas of bovine skeletal muscle reveals mechanisms regulating intramuscular adipogenesis and fibrogenesis. Journal of Cachexia Sarcopenia and Muscle. 14(5). 2152–2167. 28 indexed citations
3.
Qiao, Zhuoran, Anders S. Christensen, Matthew Welborn, et al.. (2022). OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
4.
Smith, Daniel G. A., Doaa Altarawy, Lori A. Burns, et al.. (2020). The MolSSI QCA rchive project: An open‐source platform to compute, organize, and share quantum chemistry data. Wiley Interdisciplinary Reviews Computational Molecular Science. 11(2). 58 indexed citations
5.
Voll, Constantin‐Christian A., Γεώργιος Μαρκόπουλος, Tony Wu, et al.. (2020). Lock-and-Key Exciplexes for Thermally Activated Delayed Fluorescence. SHILAP Revista de lepidopterología. 2(1). 1–10. 10 indexed citations
6.
Welborn, Matthew, et al.. (2019). Projection-Based Wavefunction-in-DFT Embedding. Accounts of Chemical Research. 52(5). 1359–1368. 111 indexed citations
7.
Cheng, Lixue, Nikola Kovachki, Matthew Welborn, & Thomas F. Miller. (2019). Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning. Journal of Chemical Theory and Computation. 15(12). 6668–6677. 46 indexed citations
8.
Welborn, Matthew, Frederick R. Manby, & Thomas F. Miller. (2018). Even-handed subsystem selection in projection-based embedding. The Journal of Chemical Physics. 149(14). 144101–144101. 36 indexed citations
9.
Welborn, Matthew, Lixue Cheng, & Thomas F. Miller. (2018). Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. Journal of Chemical Theory and Computation. 14(9). 4772–4779. 152 indexed citations
10.
Dai, Peng, Jonathan K. Williams, Chi Zhang, et al.. (2017). A structural and mechanistic study of π-clamp-mediated cysteine perfluoroarylation. Scientific Reports. 7(1). 7954–7954. 20 indexed citations
11.
Fusella, Michael A., Alyssa N. Brigeman, Matthew Welborn, et al.. (2017). Band‐like Charge Photogeneration at a Crystalline Organic Donor/Acceptor Interface. Advanced Energy Materials. 8(9). 26 indexed citations
12.
Zhang, Chi, Matthew Welborn, Tianyu Zhu, et al.. (2015). π-Clamp-mediated cysteine conjugation. Nature Chemistry. 8(2). 120–128. 255 indexed citations
13.
Wu, Mengfei, Daniel N. Congreve, Mark W. B. Wilson, et al.. (2015). Solid-state infrared-to-visible upconversion sensitized by colloidal nanocrystals. Nature Photonics. 10(1). 31–34. 457 indexed citations breakdown →
14.
Thompson, Nicholas J., Mark W. B. Wilson, Daniel N. Congreve, et al.. (2014). Energy harvesting of non-emissive triplet excitons in tetracene by emissive PbS nanocrystals. Nature Materials. 13(11). 1039–1043. 229 indexed citations breakdown →
15.
Chill, Samuel T., Matthew Welborn, Liang Zhang, et al.. (2014). EON: software for long time simulations of atomic scale systems. Modelling and Simulation in Materials Science and Engineering. 22(5). 55002–55002. 56 indexed citations
16.
Chen, Jiahao, Eric Hontz, Jeremy M. Moix, et al.. (2012). Error Analysis of Free Probability Approximations to the Density of States of Disordered Systems. Physical Review Letters. 109(3). 36403–36403. 7 indexed citations
17.
Welborn, Matthew, et al.. (2012). Database of atomistic reaction mechanisms with application to kinetic Monte Carlo. The Journal of Chemical Physics. 137(1). 14105–14105. 9 indexed citations
18.
Welborn, Matthew, Wenjie Tang, Jihoon Ryu, Valeri Petkov, & Graeme Henkelman. (2011). A combined density functional and x-ray diffraction study of Pt nanoparticle structure. The Journal of Chemical Physics. 135(1). 14503–14503. 16 indexed citations
19.
Welborn, Matthew, et al.. (2003). Multi-user perspectives in UWB communications networks. 271–275. 18 indexed citations
20.
Welborn, Matthew & Jeffrey H. Reed. (2002). Co-channel interference rejection using a model-based demodulator for AMPS and NAMPS. 2. 1312–1316. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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