John E. Herr

1.5k total citations · 2 hit papers
9 papers, 1.0k citations indexed

About

John E. Herr is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, John E. Herr has authored 9 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Materials Chemistry, 7 papers in Computational Theory and Mathematics and 4 papers in Molecular Biology. Recurrent topics in John E. Herr's work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (7 papers) and Protein Structure and Dynamics (3 papers). John E. Herr is often cited by papers focused on Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (7 papers) and Protein Structure and Dynamics (3 papers). John E. Herr collaborates with scholars based in United States, Sweden and United Kingdom. John E. Herr's co-authors include John Parkhill, Kun Yao, Sergei Rouvimov, Sergiu Draguta, Michael C. Brennan, Masaru Kuno, Jessica Zinna, Seth N. Brown, Joshua T. Horton and Peter Eastman 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 Letters.

In The Last Decade

John E. Herr

9 papers receiving 1.0k 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.

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

2018 362 citations Kun Yao, John E. Herr et al. Chemical Science profile →
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

2023 104 citations Peter Eastman, Pavan Kumar Behara et al. Scientific Data profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John E. Herr United States	8	874	370	325	232	197	9	1.0k
Igor Poltavsky Luxembourg	13	1.1k 1.3×	484 1.3×	177 0.5×	360 1.6×	324 1.6×	29	1.4k
Andrea Grisafi Switzerland	10	806 0.9×	411 1.1×	112 0.3×	211 0.9×	302 1.5×	14	960
Christopher M. Handley United Kingdom	11	620 0.7×	187 0.5×	136 0.4×	162 0.7×	256 1.3×	14	899
Jonny Proppe Germany	15	585 0.7×	223 0.6×	158 0.5×	192 0.8×	151 0.8×	33	1.0k
Valentín Vassilev-Galindo Luxembourg	10	661 0.8×	276 0.7×	91 0.3×	187 0.8×	131 0.7×	12	1.0k
Franziska Biegler Germany	6	958 1.1×	585 1.6×	106 0.3×	283 1.2×	158 0.8×	11	1.2k
Gregor N. C. Simm Switzerland	10	522 0.6×	302 0.8×	105 0.3×	204 0.9×	152 0.8×	10	801
James Chapman United States	10	671 0.8×	219 0.6×	130 0.4×	133 0.6×	90 0.5×	26	771
Zhenwei Li China	9	983 1.1×	132 0.4×	324 1.0×	114 0.5×	91 0.5×	13	1.1k
Christian Devereux United States	3	615 0.7×	414 1.1×	75 0.2×	283 1.2×	136 0.7×	4	757

Countries citing papers authored by John E. Herr

Since Specialization

Citations

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

Fields of papers citing papers by John E. Herr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John E. Herr. 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 John E. Herr. The network helps show where John E. Herr may publish in the future.

Co-authorship network of co-authors of John E. Herr

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Saebi, Mandana, John E. Herr, Zhichun Guo, et al.. (2023). On the use of real-world datasets for reaction yield prediction. Chemical Science. 14(19). 4997–5005. 80 indexed citations

2.

Eastman, Peter, Pavan Kumar Behara, David Dotson, et al.. (2023). SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Scientific Data. 10(1). 11–11. 104 indexed citations breakdown →

3.

Herr, John E., Kevin J. Koh, Kun Yao, & John Parkhill. (2019). Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences. The Journal of Chemical Physics. 151(8). 84103–84103. 7 indexed citations

4.

Herr, John E., et al.. (2018). Metadynamics for training neural network model chemistries: A competitive assessment. The Journal of Chemical Physics. 148(24). 241710–241710. 52 indexed citations

5.

Herr, John E., Kevin J. Koh, Kun Yao, & John Parkhill. (2018). Compressing physical properties of atomic species for improving predictive chemistry. arXiv (Cornell University). 19 indexed citations

6.

Yao, Kun, et al.. (2018). The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chemical Science. 9(8). 2261–2269. 362 indexed citations breakdown →

7.

Yao, Kun, John E. Herr, Seth N. Brown, & John Parkhill. (2017). Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. The Journal of Physical Chemistry Letters. 8(12). 2689–2694. 102 indexed citations

8.

Brennan, Michael C., John E. Herr, Jessica Zinna, et al.. (2017). Origin of the Size-Dependent Stokes Shift in CsPbBr₃ Perovskite Nanocrystals. Journal of the American Chemical Society. 139(35). 12201–12208. 287 indexed citations

9.

Reddy, Narender P., et al.. (1989). A Technique for Quantitative Assessment of Three-Dimensional Motion with Applications to Human Joints. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 203(4). 207–213. 3 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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