Eric D. Boittier

468 total citations
17 papers, 222 citations indexed

About

Eric D. Boittier is a scholar working on Molecular Biology, Materials Chemistry and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Eric D. Boittier has authored 17 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 8 papers in Materials Chemistry and 5 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Eric D. Boittier's work include Machine Learning in Materials Science (7 papers), Protein Structure and Dynamics (6 papers) and Spectroscopy and Quantum Chemical Studies (3 papers). Eric D. Boittier is often cited by papers focused on Machine Learning in Materials Science (7 papers), Protein Structure and Dynamics (6 papers) and Spectroscopy and Quantum Chemical Studies (3 papers). Eric D. Boittier collaborates with scholars based in Switzerland, Australia and United States. Eric D. Boittier's co-authors include Markus Meuwly, Neha S. Gandhi, Derek J. Richard, Luis Itza Vazquez-Salazar, Silvan Käser, Vito Ferro, M. Devereux, Oliver T. Unke, Mark N. Adams and Deirdre R. Coombe and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry B and International Journal of Molecular Sciences.

In The Last Decade

Eric D. Boittier

16 papers receiving 219 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eric D. Boittier Switzerland 8 91 86 62 34 33 17 222
Majda Misini Ignjatović Sweden 10 175 1.9× 44 0.5× 71 1.1× 21 0.6× 46 1.4× 14 280
Pedro B. P. S. Reis Portugal 11 203 2.2× 52 0.6× 72 1.2× 32 0.9× 28 0.8× 18 287
Dvir Doron Israel 12 237 2.6× 122 1.4× 47 0.8× 72 2.1× 32 1.0× 13 356
Mehtap Işık United States 9 115 1.3× 63 0.7× 132 2.1× 31 0.9× 95 2.9× 12 276
Ulf Börjesson Sweden 9 286 3.1× 56 0.7× 85 1.4× 85 2.5× 59 1.8× 15 382
Patrick Schöpf United States 6 108 1.2× 49 0.6× 35 0.6× 52 1.5× 16 0.5× 7 227
Ewa I. Chudyk United Kingdom 12 242 2.7× 54 0.6× 117 1.9× 24 0.7× 39 1.2× 13 405
Angélica Nakagawa Lima Brazil 8 161 1.8× 45 0.5× 128 2.1× 11 0.3× 49 1.5× 12 327
Stefania Pfeiffer‐Marek Germany 11 254 2.8× 48 0.6× 78 1.3× 7 0.2× 21 0.6× 14 329
Nicolas Froloff France 9 239 2.6× 55 0.6× 137 2.2× 34 1.0× 51 1.5× 12 377

Countries citing papers authored by Eric D. Boittier

Since Specialization
Citations

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

Fields of papers citing papers by Eric D. Boittier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric D. Boittier. 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 Eric D. Boittier. The network helps show where Eric D. Boittier may publish in the future.

Co-authorship network of co-authors of Eric D. Boittier

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

All Works

17 of 17 papers shown
1.
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
2.
Devereux, M., Eric D. Boittier, & Markus Meuwly. (2024). Systematic improvement of empirical energy functions in the era of machine learning. Journal of Computational Chemistry. 45(22). 1899–1913. 7 indexed citations
3.
Boittier, Eric D., Kai Töpfer, M. Devereux, & Markus Meuwly. (2024). Kernel-Based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations. Journal of Chemical Theory and Computation. 4 indexed citations
4.
Töpfer, Kai, et al.. (2024). Force Fields for Deep Eutectic Mixtures: Application to Structure, Thermodynamics and 2D-Infrared Spectroscopy. The Journal of Physical Chemistry B. 128(44). 10937–10949. 3 indexed citations
5.
Fisher, Mark, Eric D. Boittier, Emma Bolderson, et al.. (2023). Structural investigation of CDCA3‐Cdh1 protein–protein interactions using in vitro studies and molecular dynamics simulation. Protein Science. 32(3). e4572–e4572. 1 indexed citations
6.
Boittier, Eric D., et al.. (2023). Interaction at a distance: Xenon migration in Mb. The Journal of Chemical Physics. 158(12). 125103–125103. 5 indexed citations
7.
Boittier, Eric D., et al.. (2022). Synthesis of a Gal-ß-(1?4)-Gal disaccharide as a ligand for the fimbrial adhesin UcaD. Australian Journal of Chemistry. 76(1). 30–36.
8.
Boittier, Eric D., M. Devereux, & Markus Meuwly. (2022). Molecular Dynamics with Conformationally Dependent, Distributed Charges. Journal of Chemical Theory and Computation. 18(12). 7544–7554. 9 indexed citations
9.
Vazquez-Salazar, Luis Itza, Eric D. Boittier, & Markus Meuwly. (2022). Uncertainty quantification for predictions of atomistic neural networks. Chemical Science. 13(44). 13068–13084. 17 indexed citations
10.
Dave, Keyur A., Christopher J. Molloy, Neha S. Gandhi, et al.. (2021). Identification of Proteins Deregulated by Platinum-Based Chemotherapy as Novel Biomarkers and Therapeutic Targets in Non-Small Cell Lung Cancer. Frontiers in Oncology. 11. 615967–615967. 6 indexed citations
11.
Gandhi, Neha S., Esha T. Shah, Eric D. Boittier, et al.. (2021). Elevating CDCA3 levels in non-small cell lung cancer enhances sensitivity to platinum-based chemotherapy. Communications Biology. 4(1). 638–638. 14 indexed citations
12.
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
13.
Vazquez-Salazar, Luis Itza, Eric D. Boittier, Oliver T. Unke, & Markus Meuwly. (2021). Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies. Journal of Chemical Theory and Computation. 17(8). 4769–4785. 15 indexed citations
14.
Boittier, Eric D., et al.. (2020). GlycoTorch Vina: Docking Designed and Tested for Glycosaminoglycans. Journal of Chemical Information and Modeling. 60(12). 6328–6343. 24 indexed citations
15.
Boittier, Eric D., et al.. (2020). Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors. International Journal of Molecular Sciences. 21(15). 5183–5183. 54 indexed citations
16.
Boittier, Eric D., et al.. (2019). Pathway Bifurcation in the (4 + 3)/(5 + 2)-Cycloaddition of Butadiene and Oxidopyrylium Ylides: The Significance of Molecular Orbital Isosymmetry. The Journal of Organic Chemistry. 84(10). 5997–6005. 19 indexed citations
17.
Boittier, Eric D., Neha S. Gandhi, Vito Ferro, & Deirdre R. Coombe. (2019). Cross-Species Analysis of Glycosaminoglycan Binding Proteins Reveals Some Animal Models Are “More Equal” than Others. Molecules. 24(5). 924–924. 7 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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