Esther Heid

1.7k total citations · 1 hit paper
41 papers, 1.1k citations indexed

About

Esther Heid is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Esther Heid has authored 41 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Materials Chemistry, 13 papers in Atomic and Molecular Physics, and Optics and 10 papers in Computational Theory and Mathematics. Recurrent topics in Esther Heid's work include Machine Learning in Materials Science (13 papers), Spectroscopy and Quantum Chemical Studies (11 papers) and Computational Drug Discovery Methods (10 papers). Esther Heid is often cited by papers focused on Machine Learning in Materials Science (13 papers), Spectroscopy and Quantum Chemical Studies (11 papers) and Computational Drug Discovery Methods (10 papers). Esther Heid collaborates with scholars based in Austria, United States and United Kingdom. Esther Heid's co-authors include William H. Green, Christian Schröder, Charles J. McGill, Florence H. Vermeire, Haoyang Wu, Yunsie Chung, David Graff, Kevin P. Greenman, Shih‐Cheng Li and Klavs F. Jensen and has published in prestigious journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and The Journal of Physical Chemistry B.

In The Last Decade

Esther Heid

38 papers receiving 1.0k citations

Hit Papers

Chemprop: A Machine Learning Package for Chemical Propert... 2023 2026 2024 2025 2023 50 100 150 200 250
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. Chemprop: A Machine Learning Package for Chemical Property Prediction
    2023 250 citations Esther Heid, Kevin P. Greenman et al. Journal of Chemical Information and Modeling profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Esther Heid Austria 18 504 392 250 210 135 41 1.1k
Jonny Proppe Germany 15 585 1.2× 223 0.6× 192 0.8× 89 0.4× 85 0.6× 33 1.0k
Steven V. Jerome United States 12 450 0.9× 430 1.1× 625 2.5× 80 0.4× 75 0.6× 18 1.7k
Gregor N. C. Simm Switzerland 10 522 1.0× 302 0.8× 204 0.8× 109 0.5× 74 0.5× 10 801
Sulev Sild Estonia 21 268 0.5× 558 1.4× 265 1.1× 62 0.3× 107 0.8× 40 1.4k
Andre Lomaka Estonia 13 216 0.4× 298 0.8× 128 0.5× 338 1.6× 173 1.3× 21 971
Yanfei Guan United States 16 546 1.1× 333 0.8× 259 1.0× 85 0.4× 159 1.2× 24 1.4k
Ruslan Petrukhin United States 14 271 0.5× 449 1.1× 130 0.5× 339 1.6× 183 1.4× 17 1.1k
James L. McDonagh United Kingdom 14 415 0.8× 214 0.5× 147 0.6× 39 0.2× 94 0.7× 24 886
Alain C. Vaucher Switzerland 16 806 1.6× 517 1.3× 314 1.3× 98 0.5× 177 1.3× 34 1.2k
Álvaro Vázquez‐Mayagoitia United States 21 1.0k 2.0× 326 0.8× 185 0.7× 56 0.3× 86 0.6× 45 1.6k

Countries citing papers authored by Esther Heid

Since Specialization
Citations

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

Fields of papers citing papers by Esther Heid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Esther Heid

This figure shows the co-authorship network connecting the top 25 collaborators of Esther Heid. A scholar is included among the top collaborators of Esther Heid 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 Esther Heid. Esther Heid 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
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Müller, Simon, Esther Heid, Frank Neese, et al.. (2025). A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: Atomic polarizabilities. Chemical Engineering Science. 319. 122170–122170.
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Heid, Esther, et al.. (2025). Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states. Digital Discovery. 4(11). 3208–3216. 1 indexed citations
5.
Heid, Esther, Michele Riva, Giada Franceschi, et al.. (2024). Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning. Digital Discovery. 3(10). 2137–2145. 2 indexed citations
6.
Heid, Esther, et al.. (2024). LoGAN: local generative adversarial network for novel structure prediction. Machine Learning Science and Technology. 5(3). 35079–35079. 1 indexed citations
7.
Heid, Esther, et al.. (2024). Spatially Resolved Uncertainties for Machine Learning Potentials. Journal of Chemical Information and Modeling. 64(16). 6377–6387. 9 indexed citations
8.
Heid, Esther, Kevin P. Greenman, Yunsie Chung, et al.. (2023). Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling. 64(1). 9–17. 250 indexed citations breakdown →
9.
Carrete, Jesús, et al.. (2023). Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning. The Journal of Chemical Physics. 158(20). 38 indexed citations
10.
Zahrt, Andrew F., et al.. (2022). Machine-Learning-Guided Discovery of Electrochemical Reactions. Journal of the American Chemical Society. 144(49). 22599–22610. 43 indexed citations
11.
Heid, Esther, et al.. (2022). Collectivity in ionic liquids: a temperature dependent, polarizable molecular dynamics study. Physical Chemistry Chemical Physics. 24(26). 15776–15790. 8 indexed citations
12.
Sankaranarayanan, Karthik, Esther Heid, Connor W. Coley, et al.. (2022). Similarity based enzymatic retrosynthesis. Chemical Science. 13(20). 6039–6053. 21 indexed citations
13.
Berthin, Roxanne, Alessandra Serva, Kyle G. Reeves, et al.. (2021). Solvation of anthraquinone and TEMPO redox-active species in acetonitrile using a polarizable force field. The Journal of Chemical Physics. 155(7). 74504–74504. 12 indexed citations
14.
Heid, Esther, Samuel Goldman, Karthik Sankaranarayanan, et al.. (2021). EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. Journal of Chemical Information and Modeling. 61(10). 4949–4961. 10 indexed citations
15.
Strate, Anne, Andreas Appelhagen, Esther Heid, et al.. (2020). Understanding the Nature of Nuclear Magnetic Resonance Relaxation by Means of Fast-Field-Cycling Relaxometry and Molecular Dynamics Simulations—The Validity of Relaxation Models. The Journal of Physical Chemistry Letters. 11(6). 2165–2170. 23 indexed citations
16.
Heid, Esther, et al.. (2020). Dielectric spectroscopy and time dependent Stokes shift: two faces of the same coin?. Physical Chemistry Chemical Physics. 22(33). 18388–18399. 1 indexed citations
17.
Guan, Yanfei, Connor W. Coley, Haoyang Wu, et al.. (2020). Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors. Chemical Science. 12(6). 2198–2208. 113 indexed citations
18.
Heid, Esther, et al.. (2019). Computational spectroscopy of trehalose, sucrose, maltose, and glucose: A comprehensive study of TDSS, NQR, NOE, and DRS. The Journal of Chemical Physics. 150(17). 175102–175102. 11 indexed citations
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Chatterjee, Payal, Esther Heid, Christian Schröder, & Alexander D. MacKerell. (2019). Polarizable General Force Field for Drug-Like Molecules: Drude General Force Field (DGenFF). Biophysical Journal. 116(3). 142a–142a. 8 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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