Nicholas Lubbers
- Materials Chemistry top 2%
- Computational Theory and Mathematics top 0.5%
- Molecular Biology
- Atomic and Molecular Physics, and Optics top 5%
- Nuclear and High Energy Physics top 5%
- Co-authors
- Benjamin NebgenJustin S. SmithOlexandr IsayevKipton BarrosAdrián E. RoitbergSergei TretiakR.I. ZubatyukA. Liam Fitzpatrick
- Topics
- Machine Learning in Materials Science (37 papers)Computational Drug Discovery Methods (20 papers)Protein Structure and Dynamics (19 papers)
- Partner nations
- United StatesCyprusGermany
In The Last Decade
Nicholas Lubbers
59 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Materials Chemistry 1.9k
- Computational Theory and Mathematics 983
- Molecular Biology 626
- Atomic and Molecular Physics, and Optics 472
- Nuclear and High Energy Physics 318
Countries citing papers authored by Nicholas Lubbers
This map shows the geographic impact of Nicholas Lubbers'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 Nicholas Lubbers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas Lubbers more than expected).
Fields of papers citing papers by Nicholas Lubbers
This network shows the impact of papers produced by Nicholas Lubbers. 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 Nicholas Lubbers. The network helps show where Nicholas Lubbers may publish in the future.
Co-authorship network of co-authors of Nicholas Lubbers
This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas Lubbers. A scholar is included among the top collaborators of Nicholas Lubbers 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 Nicholas Lubbers. Nicholas Lubbers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialbreakdown → | 74 |
| 4 | 5 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 28 | |
| 8 | 30 | |
| 9 | 6 | |
| 10 | 12 | |
| 11 | 2 | |
| 12 | 14 | |
| 13 | 1 | |
| 14 | 18 | |
| 15 | 11 | |
| 16 | 5 | |
| 17 | 14 | |
| 18 | 27 | |
| 19 | Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence | 11 |
| 20 | 197 |
About Nicholas Lubbers
Nicholas Lubbers is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Statistical and Nonlinear Physics, having authored 60 papers that have together received 2.9k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (37 papers), Computational Drug Discovery Methods (20 papers) and Protein Structure and Dynamics (19 papers). The work is most often cited by research in Computational Theory and Mathematics (983 citations), Materials Chemistry (1.9k citations) and Nuclear and High Energy Physics (318 citations). Nicholas Lubbers has collaborated with scholars based in United States, Cyprus and Germany. Frequent co-authors include Benjamin Nebgen, Justin S. Smith, Olexandr Isayev, Kipton Barros, Adrián E. Roitberg, Sergei Tretiak, R.I. Zubatyuk, A. Liam Fitzpatrick, W. C. Haxton and Emanuel Katz. Their work appears in journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Nature Communications.
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.