Roman V. Krems

7.4k total citations · 2 hit papers
133 papers, 5.0k citations indexed

About

Roman V. Krems is a scholar working on Atomic and Molecular Physics, and Optics, Spectroscopy and Artificial Intelligence. According to data from OpenAlex, Roman V. Krems has authored 133 papers receiving a total of 5.0k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Atomic and Molecular Physics, and Optics, 44 papers in Spectroscopy and 15 papers in Artificial Intelligence. Recurrent topics in Roman V. Krems's work include Cold Atom Physics and Bose-Einstein Condensates (80 papers), Spectroscopy and Laser Applications (38 papers) and Quantum, superfluid, helium dynamics (35 papers). Roman V. Krems is often cited by papers focused on Cold Atom Physics and Bose-Einstein Condensates (80 papers), Spectroscopy and Laser Applications (38 papers) and Quantum, superfluid, helium dynamics (35 papers). Roman V. Krems collaborates with scholars based in Canada, United States and Russia. Roman V. Krems's co-authors include Jun Ye, Lincoln D. Carr, David DeMille, A. Dalgarno, Timur V. Tscherbul, Gerrit C. Groenenboom, Alexei A. Buchachenko, John M. Doyle, Mikhail Lemeshko and Rodrigo A. Vargas–Hernández and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Nature Communications.

In The Last Decade

Roman V. Krems

128 papers receiving 4.9k citations

Hit Papers

Cold and ultracold molecules: science, technology and app... 2008 2026 2014 2020 2009 2008 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roman V. Krems Canada 36 4.4k 1.2k 528 400 256 133 5.0k
Tak‐San Ho United States 33 3.3k 0.8× 1.1k 1.0× 583 1.1× 359 0.9× 33 0.1× 127 3.8k
Matthias Weidemüller Germany 47 6.5k 1.5× 1.3k 1.1× 1.2k 2.3× 117 0.3× 358 1.4× 175 6.9k
C. Alden Mead United States 27 3.8k 0.9× 1.0k 0.9× 204 0.4× 330 0.8× 155 0.6× 56 4.4k
George H. Booth United Kingdom 26 3.4k 0.8× 503 0.4× 960 1.8× 1.1k 2.8× 754 2.9× 64 4.5k
M. Beck Germany 21 2.4k 0.6× 753 0.6× 107 0.2× 269 0.7× 56 0.2× 75 3.4k
Nancy Makri United States 48 7.6k 1.7× 1.1k 1.0× 936 1.8× 217 0.5× 186 0.7× 165 8.0k
Bernd Hartke Germany 35 2.3k 0.5× 675 0.6× 176 0.3× 1.3k 3.2× 57 0.2× 113 3.8k
Á. Nagy Hungary 35 2.9k 0.7× 278 0.2× 251 0.5× 657 1.6× 84 0.3× 208 3.7k
Olivier Dulieu France 41 7.1k 1.6× 1.7k 1.4× 727 1.4× 110 0.3× 234 0.9× 206 7.3k
Y. B. Band Israel 38 4.6k 1.1× 880 0.8× 468 0.9× 275 0.7× 147 0.6× 233 5.5k

Countries citing papers authored by Roman V. Krems

Since Specialization
Citations

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

Fields of papers citing papers by Roman V. Krems

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roman V. Krems. 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 Roman V. Krems. The network helps show where Roman V. Krems may publish in the future.

Co-authorship network of co-authors of Roman V. Krems

This figure shows the co-authorship network connecting the top 25 collaborators of Roman V. Krems. A scholar is included among the top collaborators of Roman V. Krems 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 Roman V. Krems. Roman V. Krems 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.
Yang, Benhui, et al.. (2025). Accurate machine learning of rate coefficients for state-to-state transitions in molecular collisions. The Journal of Chemical Physics. 162(2). 1 indexed citations
2.
Tscherbul, Timur V. & Roman V. Krems. (2025). Rigorous quantum calculations for atom–molecule chemical reactions in electric fields: From single to multiple partial wave regimes. The Journal of Chemical Physics. 163(18).
3.
Krems, Roman V., et al.. (2024). Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors. Machine Learning Science and Technology. 5(1). 15059–15059.
4.
Krems, Roman V., et al.. (2024). Benchmarking of quantum fidelity kernels for Gaussian process regression. Machine Learning Science and Technology. 5(3). 35081–35081. 1 indexed citations
5.
Krems, Roman V., et al.. (2023). Neural network Gaussian processes as efficient models of potential energy surfaces for polyatomic molecules. Machine Learning Science and Technology. 4(4). 45027–45027. 4 indexed citations
6.
Krems, Roman V., et al.. (2023). Compositional optimization of quantum circuits for quantum kernels of support vector machines. Physical Review Research. 5(1). 9 indexed citations
7.
Krems, Roman V., et al.. (2023). General Classification of Qubit Encodings in Ultracold Diatomic Molecules. The Journal of Physical Chemistry A. 127(31). 6593–6602. 7 indexed citations
8.
Krems, Roman V., et al.. (2023). Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines. Nature Communications. 14(1). 576–576. 48 indexed citations
9.
Krems, Roman V., et al.. (2022). Quantum Gaussian process model of potential energy surface for a polyatomic molecule. The Journal of Chemical Physics. 156(18). 184802–184802. 9 indexed citations
10.
Otake, Yuma, et al.. (2021). Rapid and Mild One‐Flow Synthetic Approach to Unsymmetrical Sulfamides Guided by Bayesian Optimization. Chemistry - Methods. 1(11). 484–490. 29 indexed citations
11.
Tutunnikov, Ilia, et al.. (2020). Bayesian optimization for inverse problems in time-dependent quantum dynamics. The Journal of Chemical Physics. 153(16). 164111–164111. 14 indexed citations
12.
Ida, Tomonori, et al.. (2020). Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer. The Journal of Chemical Physics. 153(11). 114101–114101. 30 indexed citations
13.
Sous, John, et al.. (2018). Light Bipolarons Stabilized by Peierls Electron-Phonon Coupling. Physical Review Letters. 121(24). 247001–247001. 53 indexed citations
14.
Krems, Roman V., et al.. (2018). Effect of the anisotropy of long-range hopping on localization in three-dimensional lattices. Physical review. B.. 98(1). 12 indexed citations
15.
Vargas–Hernández, Rodrigo A., et al.. (2017). A machine-learning approach for the inverse scattering problem in quantum reaction dynamics. arXiv (Cornell University). 1 indexed citations
16.
Cui, Jie & Roman V. Krems. (2015). Gaussian Process Model for Collision Dynamics of Complex Molecules. Physical Review Letters. 115(7). 73202–73202. 46 indexed citations
17.
Krems, Roman V.. (2008). Cold Controlled Chemistry. Bulletin of the American Physical Society. 39. 5 indexed citations
18.
Balakrishnan, N., et al.. (2008). Vibrational energy transfer in ultracold molecule-molecule collisions. Bulletin of the American Physical Society. 39. 1 indexed citations
19.
Krems, Roman V.. (2002). Vibrational relaxation of vibrationally and rotationally excited CO molecules by He atoms. The Journal of Chemical Physics. 116(11). 4517–4524. 32 indexed citations
20.
Krems, Roman V. & Sture Nordholm. (2000). A Thermodynamic Method of Estimating Anharmonic Molecular Densities of States. Zeitschrift für Physikalische Chemie. 214(11). 28 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026