Richard A. Messerly

1.4k total citations · 1 hit paper
41 papers, 969 citations indexed

About

Richard A. Messerly is a scholar working on Materials Chemistry, Biomedical Engineering and Fluid Flow and Transfer Processes. According to data from OpenAlex, Richard A. Messerly has authored 41 papers receiving a total of 969 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Materials Chemistry, 19 papers in Biomedical Engineering and 10 papers in Fluid Flow and Transfer Processes. Recurrent topics in Richard A. Messerly's work include Machine Learning in Materials Science (15 papers), Phase Equilibria and Thermodynamics (15 papers) and Computational Drug Discovery Methods (9 papers). Richard A. Messerly is often cited by papers focused on Machine Learning in Materials Science (15 papers), Phase Equilibria and Thermodynamics (15 papers) and Computational Drug Discovery Methods (9 papers). Richard A. Messerly collaborates with scholars based in United States, Germany and Cyprus. Richard A. Messerly's co-authors include Daniel R. Roe, Edward J. Maginn, Sergei Tretiak, Nicholas Lubbers, Kipton Barros, Justin S. Smith, Benjamin Nebgen, Ying Wai Li, Maksim Kulichenko and Jeppe C. Dyre and has published in prestigious journals such as Chemical Reviews, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Richard A. Messerly

40 papers receiving 951 citations

Hit Papers

align trajectories

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.

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						Papers	Cites
Richard A. Messerly United States	15	515	338	168	154	152	41	969
Colin A. Grambow United States	11	712 1.4×	235 0.7×	95 0.6×	441 2.9×	194 1.3×	15	1.3k
Joshua W. Allen United States	10	370 0.7×	217 0.6×	278 1.7×	105 0.7×	349 2.3×	12	1.1k
Jinzhe Zeng United States	13	845 1.6×	134 0.4×	56 0.3×	218 1.4×	191 1.3×	19	1.3k
Daniela Polino Italy	15	298 0.6×	82 0.2×	179 1.1×	43 0.3×	198 1.3×	21	673
Georgios C. Boulougouris Greece	18	281 0.5×	683 2.0×	280 1.7×	31 0.2×	185 1.2×	43	1.2k
Wassja A. Kopp Germany	14	274 0.5×	255 0.8×	410 2.4×	39 0.3×	152 1.0×	33	860
V. P. Voloshin Russia	20	437 0.8×	242 0.7×	126 0.8×	21 0.1×	353 2.3×	66	1.1k
Qiuping Wang China	14	357 0.7×	140 0.4×	97 0.6×	41 0.3×	71 0.5×	69	911
Arnold Tharrington United States	8	483 0.9×	228 0.7×	43 0.3×	27 0.2×	198 1.3×	10	1.1k
Maria Fyta Germany	22	796 1.5×	525 1.6×	96 0.6×	29 0.2×	399 2.6×	91	1.8k

Countries citing papers authored by Richard A. Messerly

Since Specialization

Citations

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

Fields of papers citing papers by Richard A. Messerly

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard A. Messerly. 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 Richard A. Messerly. The network helps show where Richard A. Messerly may publish in the future.

Co-authorship network of co-authors of Richard A. Messerly

This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Messerly. A scholar is included among the top collaborators of Richard A. Messerly 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 Richard A. Messerly. Richard A. Messerly is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

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

Isayev, Olexandr, et al.. (2025). Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry. Journal of Chemical Information and Modeling. 65(9). 4367–4380. 2 indexed citations

Lubbers, Nicholas, et al.. (2025). Toward machine learning interatomic potentials for modeling uranium mononitride. Machine Learning Science and Technology. 6(3). 35064–35064. 1 indexed citations

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 →

Freixas, Victor M., Walter Malone, Xinyang Li, et al.. (2023). NEXMD v2.0 Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations. Journal of Chemical Theory and Computation. 19(16). 5356–5368. 18 indexed citations

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

Boothroyd, Simon, et al.. (2022). Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models. Journal of Chemical Information and Modeling. 62(4). 874–889. 11 indexed citations

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

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

10.

Kim, Yeonjoon, Brian D. Etz, Gina M. Fioroni, et al.. (2020). Investigation of structural effects of aromatic compounds on sooting tendency with mechanistic insight into ethylphenol isomers. Proceedings of the Combustion Institute. 38(1). 1143–1151. 14 indexed citations

11.

Kwon, Hyunguk, Brian D. Etz, Richard A. Messerly, et al.. (2020). Reactive Molecular Dynamics Simulations and Quantum Chemistry Calculations To Investigate Soot-Relevant Reaction Pathways for Hexylamine Isomers. The Journal of Physical Chemistry A. 124(21). 4290–4304. 15 indexed citations

12.

Etz, Brian D., Gina M. Fioroni, Richard A. Messerly, et al.. (2020). Elucidating the chemical pathways responsible for the sooting tendency of 1 and 2-phenylethanol. Proceedings of the Combustion Institute. 38(1). 1327–1334. 12 indexed citations

13.

Messerly, Richard A., et al.. (2019). Coexistence calculation using the isothermal-isochoric integration method. Fluid Phase Equilibria. 501. 112236–112236. 2 indexed citations

14.

Messerly, Richard A., et al.. (2019). Molecular Calculation of the Critical Parameters of Classical Helium. Journal of Chemical & Engineering Data. 65(3). 1028–1037. 9 indexed citations

15.

Bell, Ian H., Richard A. Messerly, Monika Thol, Lorenzo Costigliola, & Jeppe C. Dyre. (2019). Modified Entropy Scaling of the Transport Properties of the Lennard-Jones Fluid. The Journal of Physical Chemistry B. 123(29). 6345–6363. 107 indexed citations

16.

Maginn, Edward J., et al.. (2018). Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]. 1(1). 6324–6324. 211 indexed citations

17.

Messerly, Richard A., Michael R. Shirts, & Andrei F. Kazakov. (2018). Uncertainty quantification confirms unreliable extrapolation toward high pressures for united-atom Mie λ-6 force field. The Journal of Chemical Physics. 149(11). 114109–114109. 7 indexed citations

18.

Messerly, Richard A., Thomas A. Knotts, & W. Vincent Wilding. (2017). Uncertainty quantification and propagation of errors of the Lennard-Jones 12-6 parameters forn-alkanes. The Journal of Chemical Physics. 146(19). 194110–194110. 20 indexed citations

19.

Messerly, Richard A.. (2016). How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n -Alkanes. 1 indexed citations

20.

Messerly, Richard A., Richard L. Rowley, Thomas A. Knotts, & W. Vincent Wilding. (2015). An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulation. The Journal of Chemical Physics. 143(10). 104101–104101. 10 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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