Silvan Käser

657 total citations
21 papers, 341 citations indexed

About

Silvan Käser is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Silvan Käser has authored 21 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Atomic and Molecular Physics, and Optics, 12 papers in Materials Chemistry and 7 papers in Computational Theory and Mathematics. Recurrent topics in Silvan Käser's work include Machine Learning in Materials Science (12 papers), Advanced Chemical Physics Studies (8 papers) and Computational Drug Discovery Methods (7 papers). Silvan Käser is often cited by papers focused on Machine Learning in Materials Science (12 papers), Advanced Chemical Physics Studies (8 papers) and Computational Drug Discovery Methods (7 papers). Silvan Käser collaborates with scholars based in Switzerland, United States and Germany. Silvan Käser's co-authors include Markus Meuwly, Kai Töpfer, Luis Itza Vazquez-Salazar, Oliver T. Unke, Jeremy O. Richardson, Eric D. Boittier, Debasish Koner, O. Anatole von Lilienfeld, Anders S. Christensen and Narendra Singh and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Physical Chemistry Chemical Physics.

In The Last Decade

Silvan Käser

18 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Silvan Käser Switzerland 10 232 143 130 80 55 21 341
Viktor Zaverkin Germany 12 237 1.0× 82 0.6× 120 0.9× 63 0.8× 65 1.2× 14 313
Amanda Dewyer United States 5 173 0.7× 88 0.6× 98 0.8× 72 0.9× 40 0.7× 7 315
Maria Carolina Muniz United States 7 243 1.0× 90 0.6× 83 0.6× 73 0.9× 28 0.5× 7 322
Michael J. Willatt Switzerland 8 417 1.8× 200 1.4× 210 1.6× 88 1.1× 75 1.4× 11 592
Luke W. Bertels United States 11 166 0.7× 176 1.2× 63 0.5× 48 0.6× 64 1.2× 15 342
Taewon David Kim Canada 12 72 0.3× 89 0.6× 79 0.6× 60 0.8× 39 0.7× 13 265
Louis Thiry France 4 199 0.9× 116 0.8× 69 0.5× 32 0.4× 24 0.4× 6 314
Eleftherios Lambros United States 12 187 0.8× 283 2.0× 37 0.3× 91 1.1× 59 1.1× 19 417
Marius R. Bittermann Netherlands 6 220 0.9× 45 0.3× 93 0.7× 74 0.9× 23 0.4× 13 286
Elia Schneider United States 8 211 0.9× 70 0.5× 65 0.5× 119 1.5× 23 0.4× 15 441

Countries citing papers authored by Silvan Käser

Since Specialization
Citations

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

Fields of papers citing papers by Silvan Käser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silvan Käser

This figure shows the co-authorship network connecting the top 25 collaborators of Silvan Käser. A scholar is included among the top collaborators of Silvan Käser 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 Silvan Käser. Silvan Käser 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.
2.
Käser, Silvan, Jeremy O. Richardson, & Markus Meuwly. (2025). Transfer Learning for Predictive Molecular Simulations: Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy. Journal of Chemical Theory and Computation. 21(13). 6633–6643. 6 indexed citations
3.
Yin, Cangtao, et al.. (2025). Photodissociation dynamics of energized H2COO: Formation of molecular products. The Journal of Chemical Physics. 163(21).
4.
Boittier, Eric D., Silvan Käser, & Markus Meuwly. (2025). Roadmap to CCSD(T)-Quality Machine-Learned Potentials for Condensed Phase Simulations. Journal of Chemical Theory and Computation. 21(18). 8683–8698. 1 indexed citations
5.
Käser, Silvan, et al.. (2025). Reaction Dynamics of the H + HeH + → He + H 2 + System. Precision Chemistry. 3(11). 677–688. 1 indexed citations
6.
7.
Vazquez-Salazar, Luis Itza, Silvan Käser, & Markus Meuwly. (2025). Outlier-detection for reactive machine learned potential energy surfaces. npj Computational Materials. 11(1). 33–33. 3 indexed citations
8.
Käser, Silvan & Markus Meuwly. (2024). Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces. The Journal of Physical Chemistry Letters. 15(12). 3419–3424. 7 indexed citations
9.
Song, K., Silvan Käser, Kai Töpfer, Luis Itza Vazquez-Salazar, & Markus Meuwly. (2023). PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations. The Journal of Chemical Physics. 159(2). 16 indexed citations
10.
Käser, Silvan, Jia Wang, Max Schwilk, et al.. (2023). Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions. Physical Chemistry Chemical Physics. 25(20). 13933–13945. 8 indexed citations
11.
Käser, Silvan & Markus Meuwly. (2023). Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at CCSD(T) quality. The Journal of Chemical Physics. 158(21). 16 indexed citations
12.
Käser, Silvan, et al.. (2023). Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces. SHILAP Revista de lepidopterología. 2(1). 100033–100033. 2 indexed citations
13.
Käser, Silvan, Jeremy O. Richardson, & Markus Meuwly. (2022). Transfer Learning for Affordable and High-Quality Tunneling Splittings from Instanton Calculations. Journal of Chemical Theory and Computation. 18(11). 6840–6850. 31 indexed citations
14.
Käser, Silvan, Kai Töpfer, Rolf Pfister, et al.. (2022). Hydration dynamics and IR spectroscopy of 4-fluorophenol. Physical Chemistry Chemical Physics. 24(42). 26046–26060. 6 indexed citations
15.
Töpfer, Kai, Silvan Käser, & Markus Meuwly. (2022). Double proton transfer in hydrated formic acid dimer: Interplay of spatial symmetry and solvent-generated force on reactivity. Physical Chemistry Chemical Physics. 24(22). 13869–13882. 17 indexed citations
16.
Käser, Silvan, et al.. (2021). Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models. Journal of Chemical Theory and Computation. 17(6). 3687–3699. 36 indexed citations
17.
Käser, Silvan, Oliver T. Unke, & Markus Meuwly. (2020). Acetaldehyde Dataset. Zenodo (CERN European Organization for Nuclear Research). 26 indexed citations
18.
Käser, Silvan, Oliver T. Unke, & Markus Meuwly. (2020). Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces. New Journal of Physics. 22(5). 55002–55002. 43 indexed citations
19.
Käser, Silvan, Debasish Koner, Anders S. Christensen, O. Anatole von Lilienfeld, & Markus Meuwly. (2020). Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet. The Journal of Physical Chemistry A. 124(42). 8853–8865. 26 indexed citations
20.
Koner, Debasish, et al.. (2020). Machine Learning for Observables: Reactant to Product State Distributions for Atom–Diatom Collisions. The Journal of Physical Chemistry A. 124(35). 7177–7190. 16 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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