Justin S. Smith
- Materials Chemistry top 1%
- Computational Theory and Mathematics top 0.1%
- Molecular Biology top 5%
- Atomic and Molecular Physics, and Optics top 5%
- Electrical and Electronic Engineering top 10%
- Co-authors
- Olexandr IsayevAdrián E. RoitbergBenjamin NebgenNicholas LubbersR.I. ZubatyukKipton BarrosSergei TretiakChristian Devereux
- Topics
- Machine Learning in Materials Science (28 papers)Computational Drug Discovery Methods (22 papers)Spinal Fractures and Fixation Techniques (14 papers)
- Partner nations
- United StatesRussiaCanada
In The Last Decade
Justin S. Smith
63 papers receiving 4.9k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Materials Chemistry 3.6k
- Computational Theory and Mathematics 2.1k
- Molecular Biology 1.5k
- Atomic and Molecular Physics, and Optics 718
- Electrical and Electronic Engineering 423
Countries citing papers authored by Justin S. Smith
This map shows the geographic impact of Justin S. Smith'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 Justin S. Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Justin S. Smith more than expected).
Fields of papers citing papers by Justin S. Smith
This network shows the impact of papers produced by Justin S. Smith. 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 Justin S. Smith. The network helps show where Justin S. Smith may publish in the future.
Co-authorship network of co-authors of Justin S. Smith
This figure shows the co-authorship network connecting the top 25 collaborators of Justin S. Smith. A scholar is included among the top collaborators of Justin S. Smith 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 Justin S. Smith. Justin S. Smith is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialbreakdown → | 74 |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 14 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 18 | |
| 10 | 83 | |
| 11 | 197 | |
| 12 | 45 | |
| 13 | 2 | |
| 14 | The Aging of the Global Populationbreakdown → | 269 |
| 15 | CADENCE for Collaboration and Companionship with Robots. | 1 |
| 16 | 15 | |
| 17 | 108 | |
| 18 | 17 | |
| 19 | 21 | |
| 20 | 62 |
About Justin S. Smith
Justin S. Smith is a scholar working on Computational Theory and Mathematics, Physical and Theoretical Chemistry and Pathology and Forensic Medicine, having authored 68 papers that have together received 5.0k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (28 papers), Computational Drug Discovery Methods (22 papers) and Spinal Fractures and Fixation Techniques (14 papers). The work is most often cited by research in Computational Theory and Mathematics (2.1k citations), Materials Chemistry (3.6k citations) and Physical and Theoretical Chemistry (409 citations). Justin S. Smith has collaborated with scholars based in United States, Russia and Canada. Frequent co-authors include Olexandr Isayev, Adrián E. Roitberg, Benjamin Nebgen, Nicholas Lubbers, R.I. Zubatyuk, Kipton Barros, Sergei Tretiak, Christian Devereux, Jerzy Leszczyński and Xiang Gao. Their work appears in journals such as Chemical Reviews, Nature Communications and The Journal of Chemical Physics.
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.