Mark E. Tuckerman

39.9k total citations · 15 hit papers
225 papers, 29.5k citations indexed

About

Mark E. Tuckerman is a scholar working on Atomic and Molecular Physics, and Optics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Mark E. Tuckerman has authored 225 papers receiving a total of 29.5k indexed citations (citations by other indexed papers that have themselves been cited), including 134 papers in Atomic and Molecular Physics, and Optics, 48 papers in Molecular Biology and 46 papers in Materials Chemistry. Recurrent topics in Mark E. Tuckerman's work include Spectroscopy and Quantum Chemical Studies (106 papers), Advanced Chemical Physics Studies (64 papers) and Quantum, superfluid, helium dynamics (55 papers). Mark E. Tuckerman is often cited by papers focused on Spectroscopy and Quantum Chemical Studies (106 papers), Advanced Chemical Physics Studies (64 papers) and Quantum, superfluid, helium dynamics (55 papers). Mark E. Tuckerman collaborates with scholars based in United States, China and Germany. Mark E. Tuckerman's co-authors include Glenn Martyna, Michael L. Klein, Michele Parrinello, B. J. Berne, Dominik Marx, Jürg Hutter, Douglas J. Tobias, Hee‐Seung Lee, Michiel Sprik and Kari Laasonen and has published in prestigious journals such as Nature, Science and Chemical Reviews.

In The Last Decade

Mark E. Tuckerman

217 papers receiving 29.0k citations

Hit Papers

Nosé–Hoover chains: The canonical ensemble via continuous... 1992 2026 2003 2014 1992 1992 2020 1999 1996 1000 2.0k 3.0k 4.0k
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. Nosé–Hoover chains: The canonical ensemble via continuous dynamics
    1992 4.6k citations Mark E. Tuckerman et al. profile →
  2. Reversible multiple time scale molecular dynamics
    1992 2.9k citations Mark E. Tuckerman et al. profile →
  3. Deep Eutectic Solvents: A Review of Fundamentals and Applications
    2020 2.3k citations Benworth Hansen, Stephanie Spittle et al. Chemical Reviews profile →
  4. The nature of the hydrated excess proton in water
    1999 1.5k citations Mark E. Tuckerman et al. profile →
  5. Explicit reversible integrators for extended systems dynamics
    1996 1.4k citations Mark E. Tuckerman et al. profile →
  6. The nature and transport mechanism of hydrated hydroxide ions in aqueous solution
    2002 862 citations Mark E. Tuckerman et al. profile →
  7. Ab initio molecular dynamics simulation of the solvation and transport of hydronium and hydroxyl ions in water
    1995 741 citations Mark E. Tuckerman et al. profile →
  8. On the Quantum Nature of the Shared Proton in Hydrogen Bonds
    1997 617 citations Mark E. Tuckerman et al. profile →
  9. A reciprocal space based method for treating long range interactions in ab initio and force-field-based calculations in clusters
    1999 560 citations Mark E. Tuckerman et al. profile →
  10. Ab Initio Molecular Dynamics Simulation of the Solvation and Transport of H3O+ and OH- Ions in Water
    1995 534 citations Mark E. Tuckerman et al. profile →
  11. The mechanism of proton conduction in phosphoric acid
    2012 488 citations Mark E. Tuckerman et al. profile →
  12. A Liouville-operator derived measure-preserving integrator for molecular dynamics simulations in the isothermal–isobaric ensemble
    2006 474 citations Mark E. Tuckerman et al. profile →
  13. Aqueous Basic Solutions: Hydroxide Solvation, Structural Diffusion, and Comparison to the Hydrated Proton
    2010 411 citations Mark E. Tuckerman et al. Chemical Reviews profile →
  14. Quantum chemical accuracy from density functional approximations via machine learning
    2020 264 citations Leslie Vogt-Maranto, Mark E. Tuckerman et al. Nature Communications profile →
  15. Statistical Mechanics: Theory and Molecular Simulation
    2023 184 citations Mark E. Tuckerman profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark E. Tuckerman United States 72 12.4k 8.5k 5.4k 4.6k 4.1k 225 29.5k
Gregory A. Voth United States 101 17.8k 1.4× 9.6k 1.1× 14.0k 2.6× 4.3k 0.9× 5.0k 1.2× 657 42.1k
Jürg Hutter Switzerland 64 14.5k 1.2× 16.3k 1.9× 2.6k 0.5× 8.0k 1.7× 3.5k 0.8× 194 37.4k
Mischa Bonn Germany 90 12.6k 1.0× 10.7k 1.3× 3.5k 0.7× 9.1k 2.0× 4.3k 1.0× 614 30.3k
Dominik Marx Germany 68 11.2k 0.9× 6.0k 0.7× 2.1k 0.4× 3.1k 0.7× 2.1k 0.5× 304 19.5k
Alexandre Tkatchenko Luxembourg 69 8.7k 0.7× 17.0k 2.0× 3.0k 0.6× 5.3k 1.1× 2.5k 0.6× 241 25.8k
M. D. Fayer United States 85 18.4k 1.5× 5.6k 0.7× 3.9k 0.7× 3.1k 0.7× 2.3k 0.5× 520 27.4k
Julian Tirado‐Rives United States 46 6.1k 0.5× 7.5k 0.9× 12.9k 2.4× 3.0k 0.6× 3.9k 0.9× 100 31.2k
Theresa L. Windus United States 32 12.3k 1.0× 8.8k 1.0× 3.4k 0.6× 3.6k 0.8× 1.3k 0.3× 136 30.6k
Glenn Martyna United States 46 7.1k 0.6× 6.8k 0.8× 5.9k 1.1× 2.4k 0.5× 3.1k 0.7× 143 20.9k
Shūichi Nosé Japan 19 6.8k 0.5× 12.9k 1.5× 5.8k 1.1× 3.8k 0.8× 4.2k 1.0× 42 28.7k

Countries citing papers authored by Mark E. Tuckerman

Since Specialization
Citations

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

Fields of papers citing papers by Mark E. Tuckerman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark E. Tuckerman. 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 Mark E. Tuckerman. The network helps show where Mark E. Tuckerman may publish in the future.

Co-authorship network of co-authors of Mark E. Tuckerman

This figure shows the co-authorship network connecting the top 25 collaborators of Mark E. Tuckerman. A scholar is included among the top collaborators of Mark E. Tuckerman 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 Mark E. Tuckerman. Mark E. Tuckerman 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.
Kilgour, Michael, Mark E. Tuckerman, & Jutta Rogal. (2025). Multi-type point cloud autoencoder: a complete equivariant embedding for molecule conformation and pose. Machine Learning Science and Technology. 6(3). 35055–35055.
2.
Tuckerman, Mark E., et al.. (2025). Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials. Journal of Chemical Theory and Computation. 22(1). 305–317.
3.
Wang, Yuanqing, Michael S. Chen, Marcus Wieder, et al.. (2025). On the design space between molecular mechanics and machine learning force fields. Applied Physics Reviews. 12(2). 10 indexed citations
4.
Zelovich, Tamar, Dario R. Dekel, & Mark E. Tuckerman. (2024). Electrostatic Potential of Functional Cations as a Predictor of Hydroxide Diffusion Pathways in Nanoconfined Environments of Anion Exchange Membranes. The Journal of Physical Chemistry Letters. 15(2). 408–415. 4 indexed citations
5.
Doherty, Brian, et al.. (2024). Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss‐Type Proton Transport. Advanced Materials Interfaces. 11(36). 3 indexed citations
6.
Kilgour, Michael, et al.. (2024). Machine Learning Classification of Local Environments in Molecular Crystals. Journal of Chemical Theory and Computation. 20(14). 6197–6206. 4 indexed citations
7.
Galanakis, Nikolaos & Mark E. Tuckerman. (2024). Rapid prediction of molecular crystal structures using simple topological and physical descriptors. Nature Communications. 15(1). 9757–9757. 11 indexed citations
8.
Chan, Eric J. & Mark E. Tuckerman. (2024). Polymorph sampling with coupling to extended variables: enhanced sampling of polymorph energy landscapes and free energy perturbation of polymorph ensembles. Acta Crystallographica Section B Structural Science Crystal Engineering and Materials. 80(6). 575–594. 2 indexed citations
9.
Zelovich, Tamar, Dario R. Dekel, & Mark E. Tuckerman. (2023). Functional groups in anion exchange membranes: Insights from Ab initio molecular dynamics. Journal of Membrane Science. 678. 121638–121638. 8 indexed citations
10.
Shao, Xuecheng, et al.. (2023). Machine learning electronic structure methods based on the one-electron reduced density matrix. Nature Communications. 14(1). 6281–6281. 27 indexed citations
11.
Dongare, Saudagar, et al.. (2023). Tailoring Electrochemical CO2 Reduction on Copper by Reactive Ionic Liquid and Native Hydrogen Bond Donors. Angewandte Chemie International Edition. 63(1). e202312163–e202312163. 22 indexed citations
12.
Kilgour, Michael, Jutta Rogal, & Mark E. Tuckerman. (2023). Geometric Deep Learning for Molecular Crystal Structure Prediction. Journal of Chemical Theory and Computation. 19(14). 4743–4756. 29 indexed citations
13.
Abreu, Charlles R. A., et al.. (2023). An interoperable implementation of collective‐variable based enhanced sampling methods in extended phase space within the OpenMM package. Journal of Computational Chemistry. 44(28). 2166–2183. 6 indexed citations
14.
Hong, Richard S., Alessandra Mattei, Ahmad Y. Sheikh, & Mark E. Tuckerman. (2022). A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates. Proceedings of the National Academy of Sciences. 119(43). e2204414119–e2204414119. 7 indexed citations
15.
Zhu, Xiaolong, Chunhua Hu, Leslie Vogt-Maranto, et al.. (2021). Imidacloprid Crystal Polymorphs for Disease Vector Control and Pollinator Protection. Journal of the American Chemical Society. 143(41). 17144–17152. 42 indexed citations
16.
Shtukenberg, Alexander G., Eric J. Chan, Leslie Vogt-Maranto, et al.. (2020). Disorderly Conduct of Benzamide IV: Crystallographic and Computational Analysis of High Entropy Polymorphs of Small Molecules. Crystal Growth & Design. 20(4). 2670–2682. 25 indexed citations
17.
Schneider, Elia, et al.. (2020). Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables. The Journal of Physical Chemistry B. 124(18). 3647–3660. 22 indexed citations
18.
Hansen, Benworth, Stephanie Spittle, Brian Chen, et al.. (2020). Deep Eutectic Solvents: A Review of Fundamentals and Applications. Chemical Reviews. 121(3). 1232–1285. 2333 indexed citations breakdown →
19.
Rogal, Jutta, Elia Schneider, & Mark E. Tuckerman. (2019). Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations. Physical Review Letters. 123(24). 245701–245701. 54 indexed citations
20.
Rosso, Lula & Mark E. Tuckerman. (2004). Solid-state proton conduction: An ab initio molecular dynamics investigation of ammonium perchlorate doped with neutral ammonia. Pure and Applied Chemistry. 76(1). 49–61. 6 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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