Simon Batzner

3.8k total citations · 4 hit papers
17 papers, 2.3k citations indexed

About

Simon Batzner is a scholar working on Materials Chemistry, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Simon Batzner has authored 17 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Materials Chemistry, 7 papers in Molecular Biology and 3 papers in Artificial Intelligence. Recurrent topics in Simon Batzner's work include Machine Learning in Materials Science (16 papers), Protein Structure and Dynamics (7 papers) and Computational Drug Discovery Methods (3 papers). Simon Batzner is often cited by papers focused on Machine Learning in Materials Science (16 papers), Protein Structure and Dynamics (7 papers) and Computational Drug Discovery Methods (3 papers). Simon Batzner collaborates with scholars based in United States, United Kingdom and Denmark. Simon Batzner's co-authors include Boris Kozinsky, Albert Musaelian, Lixin Sun, Mordechai Kornbluth, Nicola Molinari, Jonathan P. Mailoa, Tess Smidt, Ekin D. Cubuk, Mario Geiger and Amil Merchant and has published in prestigious journals such as Nature, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Simon Batzner

17 papers receiving 2.2k citations

Hit Papers

E(3)-equivariant graph neural networks for data-efficient... 2022 2026 2023 2024 2022 2023 2023 2025 250 500 750 1000
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. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
    2022 1.0k citations Simon Batzner, Albert Musaelian et al. Nature Communications profile →
  2. Scaling deep learning for materials discovery
    2023 667 citations Amil Merchant, Simon Batzner et al. Nature profile →
  3. Learning local equivariant representations for large-scale atomistic dynamics
    2023 370 citations Albert Musaelian, Simon Batzner et al. Nature Communications profile →
  4. The design space of E(3)-equivariant atom-centred interatomic potentials
    2025 61 citations Simon Batzner, Dávid Péter Kovács et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simon Batzner United States 11 1.8k 527 411 373 202 17 2.3k
Lixin Sun United States 18 2.1k 1.1× 421 0.8× 640 1.6× 328 0.9× 192 1.0× 37 2.6k
Mordechai Kornbluth United States 10 1.3k 0.7× 420 0.8× 315 0.8× 314 0.8× 202 1.0× 16 1.7k
Albert Musaelian United States 10 1.4k 0.8× 449 0.9× 271 0.7× 319 0.9× 188 0.9× 13 1.7k
Jon Paul Janet Sweden 23 1.6k 0.9× 781 1.5× 214 0.5× 404 1.1× 141 0.7× 45 2.2k
Tess Smidt United States 13 1.3k 0.7× 328 0.6× 332 0.8× 227 0.6× 233 1.2× 22 1.9k
Chenru Duan United States 25 1.4k 0.8× 567 1.1× 238 0.6× 245 0.7× 293 1.5× 60 2.0k
Samuel S. Schoenholz United States 18 2.0k 1.1× 391 0.7× 280 0.7× 214 0.6× 251 1.2× 36 2.8k
Nicola Molinari United States 14 1.0k 0.6× 287 0.5× 653 1.6× 212 0.6× 151 0.7× 23 1.8k
Nongnuch Artrith United States 25 2.5k 1.4× 576 1.1× 1.0k 2.5× 296 0.8× 414 2.0× 39 3.4k
James R. Kermode United Kingdom 24 2.3k 1.3× 480 0.9× 392 1.0× 319 0.9× 490 2.4× 54 2.9k

Countries citing papers authored by Simon Batzner

Since Specialization
Citations

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

Fields of papers citing papers by Simon Batzner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Batzner

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Batzner. A scholar is included among the top collaborators of Simon Batzner 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 Simon Batzner. Simon Batzner 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.
Batzner, Simon, Dávid Péter Kovács, Albert Musaelian, et al.. (2025). The design space of E(3)-equivariant atom-centred interatomic potentials. Nature Machine Intelligence. 7(1). 56–67. 61 indexed citations breakdown →
2.
Aykol, Muratahan, Amil Merchant, Simon Batzner, Jennifer N. Wei, & Ekin D. Cubuk. (2024). Predicting emergence of crystals from amorphous precursors with deep learning potentials. Nature Computational Science. 5(2). 105–111. 4 indexed citations
3.
Batzner, Simon, Albert Musaelian, Pin-Wen Guan, et al.. (2024). Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials. ACS Omega. 9(9). 10904–10912. 11 indexed citations
4.
Miura, Akira, Muratahan Aykol, S Kozaki, et al.. (2024). Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions. Journal of the American Chemical Society. 146(43). 29637–29644. 2 indexed citations
5.
Goodwin, Zachary A. H., Malia B. Wenny, Julia H. Yang, et al.. (2024). Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials. The Journal of Physical Chemistry Letters. 15(30). 7539–7547. 20 indexed citations
6.
Owen, Cameron J., Steven B. Torrisi, Yu Xie, et al.. (2024). Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set. npj Computational Materials. 10(1). 23 indexed citations
7.
Aykol, Muratahan, Simon Batzner, Ekin D. Cubuk, et al.. (2024). Generative Hierarchical Materials Search. 38799–38819. 1 indexed citations
8.
Musaelian, Albert, Simon Batzner, Anders Johansson, et al.. (2023). Learning local equivariant representations for large-scale atomistic dynamics. Nature Communications. 14(1). 579–579. 370 indexed citations breakdown →
9.
Batzner, Simon, Albert Musaelian, & Boris Kozinsky. (2023). Advancing molecular simulation with equivariant interatomic potentials. Nature Reviews Physics. 5(8). 437–438. 21 indexed citations
10.
Kozinsky, Boris, Albert Musaelian, Anders Johansson, & Simon Batzner. (2023). Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size. 1–12. 24 indexed citations
11.
Batzner, Simon. (2023). Biasing energy surfaces towards the unknown. Nature Computational Science. 3(3). 190–191. 3 indexed citations
12.
Merchant, Amil, Simon Batzner, Samuel S. Schoenholz, et al.. (2023). Scaling deep learning for materials discovery. Nature. 624(7990). 80–85. 667 indexed citations breakdown →
13.
Batzner, Simon, et al.. (2023). Fast uncertainty estimates in deep learning interatomic potentials. The Journal of Chemical Physics. 158(16). 49 indexed citations
14.
Sun, Lixin, Jonathan Vandermause, Simon Batzner, et al.. (2022). Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events. Journal of Chemical Theory and Computation. 18(4). 2341–2353. 31 indexed citations
15.
Batzner, Simon, Albert Musaelian, Lixin Sun, et al.. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications. 13(1). 2453–2453. 1022 indexed citations breakdown →
16.
Geiger, Mario, Tess Smidt, B. Miller, et al.. (2021). e3nn/e3nn: 2021-06-21. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
17.
Vandermause, Jonathan, Steven B. Torrisi, Simon Batzner, Alexie M. Kolpak, & Boris Kozinsky. (2019). Accelerating atomistic modelling with active learning. Bulletin of the American Physical Society. 2019. 1 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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