Nathan O. Hodas
- Materials Chemistry
- Computational Theory and Mathematics top 2%
- Molecular Biology
- Artificial Intelligence top 5%
- Sociology and Political Science top 10%
- Co-authors
- Garrett B. GohAbhinav VishnuSvitlana VolkovaKyle ShafferJin Yea JangCharles SiegelCourtney D. CorleyWilton Mui
- Topics
- Computational Drug Discovery Methods (6 papers)Machine Learning in Materials Science (5 papers)Complex Network Analysis Techniques (4 papers)
- Partner nations
- United StatesDenmarkEgypt
In The Last Decade
Nathan O. Hodas
22 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 145
- Materials Chemistry 321
- Computational Theory and Mathematics 302
- Molecular Biology 273
- Artificial Intelligence 217
- Sociology and Political Science 207
Countries citing papers authored by Nathan O. Hodas
This map shows the geographic impact of Nathan O. Hodas'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 Nathan O. Hodas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan O. Hodas more than expected).
Fields of papers citing papers by Nathan O. Hodas
This network shows the impact of papers produced by Nathan O. Hodas. 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 Nathan O. Hodas. The network helps show where Nathan O. Hodas may publish in the future.
Co-authorship network of co-authors of Nathan O. Hodas
This figure shows the co-authorship network connecting the top 25 collaborators of Nathan O. Hodas. A scholar is included among the top collaborators of Nathan O. Hodas 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 Nathan O. Hodas. Nathan O. Hodas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 12 | |
| 2 | 76 | |
| 3 | 1 | |
| 4 | 7 | |
| 5 | 60 | |
| 6 | SMILES2vec: Predicting Chemical Properties from Text Representations | 9 |
| 7 | ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction | 6 |
| 8 | 11 | |
| 9 | 207 | |
| 10 | Deep learning for computational chemistrybreakdown → | 555 |
| 11 | 1 | |
| 12 | 82 | |
| 13 | 4 | |
| 14 | 10 | |
| 15 | 13 | |
| 16 | 11 | |
| 17 | 15 | |
| 18 | 2 | |
| 19 | 14 | |
| 20 | 2 |
About Nathan O. Hodas
Nathan O. Hodas is a scholar working on Biophysics, General Decision Sciences and Computational Theory and Mathematics, having authored 23 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (5 papers) and Complex Network Analysis Techniques (4 papers). The work is most often cited by research in Computational Theory and Mathematics (302 citations), Health, Toxicology and Mutagenesis (114 citations) and Materials Chemistry (321 citations). Nathan O. Hodas has collaborated with scholars based in United States, Denmark and Egypt. Frequent co-authors include Garrett B. Goh, Abhinav Vishnu, Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, Charles Siegel, Courtney D. Corley, Wilton Mui, Richard C. Flagan and Andreas Zuend. Their work appears in journals such as Nucleic Acids Research, Analytical Chemistry and Scientific Reports.
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.