Benjamin Nebgen

4.2k total citations · 4 hit papers
44 papers, 2.7k citations indexed

About

Benjamin Nebgen is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Benjamin Nebgen has authored 44 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Materials Chemistry, 18 papers in Computational Theory and Mathematics and 17 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Benjamin Nebgen's work include Machine Learning in Materials Science (27 papers), Computational Drug Discovery Methods (18 papers) and Protein Structure and Dynamics (14 papers). Benjamin Nebgen is often cited by papers focused on Machine Learning in Materials Science (27 papers), Computational Drug Discovery Methods (18 papers) and Protein Structure and Dynamics (14 papers). Benjamin Nebgen collaborates with scholars based in United States, Argentina and Germany. Benjamin Nebgen's co-authors include Justin S. Smith, Nicholas Lubbers, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak, Kipton Barros, R.I. Zubatyuk, Andrew E. Sifain, Christian Devereux and Sebastian Fernández-Alberti and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Benjamin Nebgen

43 papers receiving 2.7k citations

Hit Papers

Less is more: Sampling chemical space with active learning 2018 2026 2020 2023 2018 2019 2020 2024 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Less is more: Sampling chemical space with active learning
    2018 552 citations Justin S. Smith, Benjamin Nebgen et al. The Journal of Chemical Physics profile →
  2. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
    2019 480 citations Justin S. Smith, Benjamin Nebgen et al. Nature Communications profile →
  3. Non-adiabatic Excited-State Molecular Dynamics: Theory and Applications for Modeling Photophysics in Extended Molecular Materials
    2020 320 citations Tammie Nelson, Alexander White et al. profile →
  4. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
    2024 74 citations Shuhao Zhang, Ryan B. Jadrich et al. Nature Chemistry profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Benjamin Nebgen United States 21 1.9k 968 824 686 378 44 2.7k
Raghunathan Ramakrishnan India 14 2.5k 1.3× 1.7k 1.7× 535 0.6× 780 1.1× 380 1.0× 34 3.1k
Huziel E. Sauceda Mexico 13 2.6k 1.4× 1.2k 1.3× 468 0.6× 815 1.2× 198 0.5× 24 3.0k
Stefan Chmiela Germany 13 2.5k 1.3× 1.3k 1.4× 420 0.5× 893 1.3× 220 0.6× 20 3.1k
Michael Gastegger Germany 14 1.7k 0.9× 877 0.9× 376 0.5× 583 0.8× 160 0.4× 24 2.2k
Pavlo O. Dral China 28 3.1k 1.6× 1.8k 1.8× 1.2k 1.4× 993 1.4× 566 1.5× 82 4.3k
David M. Wilkins United Kingdom 17 1.1k 0.6× 481 0.5× 706 0.9× 292 0.4× 196 0.5× 26 2.0k
Oliver T. Unke Switzerland 17 1.3k 0.7× 707 0.7× 431 0.5× 499 0.7× 138 0.4× 31 1.7k
Sandip De Switzerland 17 1.7k 0.9× 581 0.6× 282 0.3× 278 0.4× 202 0.5× 39 2.2k
Kristof T. Schütt Germany 14 4.0k 2.1× 2.1k 2.1× 625 0.8× 1.2k 1.8× 327 0.9× 23 4.9k

Countries citing papers authored by Benjamin Nebgen

Since Specialization
Citations

This map shows the geographic impact of Benjamin Nebgen'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 Benjamin Nebgen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Nebgen more than expected).

Fields of papers citing papers by Benjamin Nebgen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Benjamin Nebgen. 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 Benjamin Nebgen. The network helps show where Benjamin Nebgen may publish in the future.

Co-authorship network of co-authors of Benjamin Nebgen

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Nebgen. A scholar is included among the top collaborators of Benjamin Nebgen 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 Benjamin Nebgen. Benjamin Nebgen 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.
Nebgen, Benjamin, et al.. (2025). Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation. Journal of Chemical Information and Modeling. 65(3). 1198–1210. 6 indexed citations
2.
Li, Cheng-Han, Mehmet Cagri Kaymak, Maksim Kulichenko, et al.. (2025). Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential. Journal of Chemical Theory and Computation. 21(7). 3658–3675. 1 indexed citations
3.
Messerly, M. J., Alice Allen, Benjamin Nebgen, et al.. (2025). Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need*. Machine Learning Science and Technology. 6(3). 35066–35066. 1 indexed citations
4.
Zhang, Shuhao, Ryan B. Jadrich, Elfi Kraka, et al.. (2024). Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. Nature Chemistry. 16(5). 727–734. 74 indexed citations breakdown →
5.
Lubbers, Nicholas, et al.. (2023). Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles. The Journal of Physical Chemistry A. 127(17). 3768–3778. 6 indexed citations
6.
Fedik, Nikita, Benjamin Nebgen, Nicholas Lubbers, et al.. (2023). Synergy of semiempirical models and machine learning in computational chemistry. The Journal of Chemical Physics. 159(11). 12 indexed citations
7.
Smith, Justin S., et al.. (2023). Lightweight and effective tensor sensitivity for atomistic neural networks. The Journal of Chemical Physics. 158(18). 14 indexed citations
8.
Nebgen, Benjamin, Kipton Barros, Justin S. Smith, et al.. (2023). Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials. AIP conference proceedings. 2844. 320002–320002. 1 indexed citations
9.
Fedik, Nikita, R.I. Zubatyuk, Maksim Kulichenko, et al.. (2022). Extending machine learning beyond interatomic potentials for predicting molecular properties. Nature Reviews Chemistry. 6(9). 653–672. 94 indexed citations
10.
Sifain, Andrew E., Levi Lystrom, Richard A. Messerly, et al.. (2021). Predicting phosphorescence energies and inferring wavefunction localization with machine learning. Chemical Science. 12(30). 10207–10217. 18 indexed citations
11.
Malone, Walter, et al.. (2021). Bond order predictions using deep neural networks. Journal of Applied Physics. 129(6). 11 indexed citations
12.
Zubatyuk, R.I., Justin S. Smith, Benjamin Nebgen, Sergei Tretiak, & Olexandr Isayev. (2021). Teaching a neural network to attach and detach electrons from molecules. Nature Communications. 12(1). 4870–4870. 83 indexed citations
13.
Kulichenko, Maksim, Justin S. Smith, Benjamin Nebgen, et al.. (2021). The Rise of Neural Networks for Materials and Chemical Dynamics. The Journal of Physical Chemistry Letters. 12(26). 6227–6243. 66 indexed citations
14.
Malone, Walter, Benjamin Nebgen, Alexander White, et al.. (2020). NEXMD Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations. Journal of Chemical Theory and Computation. 16(9). 5771–5783. 69 indexed citations
15.
Zhou, Guoqing, Benjamin Nebgen, Nicholas Lubbers, et al.. (2020). Graphics Processing Unit-Accelerated Semiempirical Born Oppenheimer Molecular Dynamics Using PyTorch. Journal of Chemical Theory and Computation. 16(8). 4951–4962. 27 indexed citations
16.
Smith, Justin S., R.I. Zubatyuk, Benjamin Nebgen, et al.. (2020). The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules. Scientific Data. 7(1). 134–134. 197 indexed citations
17.
Sio, Antonietta De, Ephraim Sommer, Benjamin Nebgen, et al.. (2020). Intermolecular conical intersections in molecular aggregates. Nature Nanotechnology. 16(1). 63–68. 26 indexed citations
18.
Sifain, Andrew E., Josiah A. Bjorgaard, Tammie Nelson, et al.. (2018). Photoexcited Nonadiabatic Dynamics of Solvated Push–Pull π-Conjugated Oligomers with the NEXMD Software. Journal of Chemical Theory and Computation. 14(8). 3955–3966. 43 indexed citations
19.
Kidwell, Nathanael M., Benjamin Nebgen, Lyudmila V. Slipchenko, et al.. (2014). Vibronic coupling in asymmetric bichromophores: Experimental investigation of diphenylmethane-d5. The Journal of Chemical Physics. 141(6). 64316–64316. 13 indexed citations
20.
Berger, Robert, et al.. (2008). Laves Phases, γ‐Brass, and 2×2×2 Superstructures: A New Class of Quasicrystal Approximants and the Suggestion of a New Quasicrystal. Chemistry - A European Journal. 14(22). 6627–6639. 17 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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