Daniel Wigh
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Machine Learning in Materials Science 3
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- Metabolomics and Mass Spectrometry Studies 2
- Co-authors
- Alexei A. Lapkin (4 shared papers)Jonathan M. Goodman (2 shared papers)Kobi Felton (2 shared papers)Connor J. Taylor (1 shared paper)Rachel Grainger (1 shared paper)Christopher N. Johnson (1 shared paper)Gianni Chessari (1 shared paper)Alexander Pomberger (1 shared paper)
- Journals
- The Journal of Physical Chemistry A (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)Journal of Chemical Information and Modeling (1 paper)ACS Central Science (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United KingdomSouth SudanSingapore
In The Last Decade
Daniel Wigh
5 papers receiving 316 citations
Daniel Wigh's Hit Papers
Peers
Comparison fields: 5 of 76
- Computational Theory and Mathematics 159
- Materials Chemistry 173
- Biomedical Engineering 65
- Molecular Biology 88
- Catalysis 9
Countries citing papers authored by Daniel Wigh
This map shows the geographic impact of Daniel Wigh'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 Daniel Wigh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Wigh more than expected).
Fields of papers citing papers by Daniel Wigh
This network shows the impact of papers produced by Daniel Wigh. 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 Daniel Wigh. The network helps show where Daniel Wigh may publish in the future.
Co-authors
The 13 scholars most cited alongside Daniel Wigh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A review of molecular representation in the age of machine learning Hit paper breakdown → | 2022 | 230 |
| 2 | 2023 | 72 | |
| 3 | 2024 | 14 | |
| 4 | 2019 | 2 | |
| 5 | 2023 | 2 |
About Daniel Wigh
Daniel Wigh is a scholar working on Materials Chemistry, Molecular Biology, Organic Chemistry, Control and Systems Engineering and Information Systems and Management, having authored 5 papers that have together received 320 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Water-Energy-Food Nexus Studies (1 paper), Scientific Computing and Data Management (1 paper), Computational Drug Discovery Methods (1 paper), Asymmetric Hydrogenation and Catalysis (1 paper), Anaerobic Digestion and Biogas Production (1 paper) and Smart Grid Energy Management (1 paper). The work is most often cited by research in Computational Theory and Mathematics (159 citations), Materials Chemistry (173 citations), Biomedical Engineering (65 citations), Molecular Biology (88 citations) and Catalysis (9 citations). Daniel Wigh has collaborated with scholars based in United Kingdom, South Sudan and Singapore. Frequent co-authors include Alexei A. Lapkin, Jonathan M. Goodman, Kobi Felton, Connor J. Taylor, Rachel Grainger, Christopher N. Johnson, Gianni Chessari, Alexander Pomberger, Marco Napoli and Adam Hawkes. Their work appears in journals such as The Journal of Physical Chemistry A, Wiley Interdisciplinary Reviews Computational Molecular Science, Journal of Chemical Information and Modeling, ACS Central Science and DOAJ (DOAJ: Directory of Open Access Journals).
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.