Stefan Chmiela

5.5k total citations · 5 hit papers
20 papers, 3.1k citations indexed

About

Stefan Chmiela is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Stefan Chmiela has authored 20 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Materials Chemistry, 11 papers in Molecular Biology and 9 papers in Computational Theory and Mathematics. Recurrent topics in Stefan Chmiela's work include Machine Learning in Materials Science (19 papers), Protein Structure and Dynamics (11 papers) and Computational Drug Discovery Methods (9 papers). Stefan Chmiela is often cited by papers focused on Machine Learning in Materials Science (19 papers), Protein Structure and Dynamics (11 papers) and Computational Drug Discovery Methods (9 papers). Stefan Chmiela collaborates with scholars based in Germany, South Korea and Luxembourg. Stefan Chmiela's co-authors include Alexandre Tkatchenko, Kristof T. Schütt, Klaus‐Robert Müller, Huziel E. Sauceda, K. Müller, Igor Poltavsky, Michael Gastegger, Valentín Vassilev-Galindo, Bingqing Cheng and John A. Keith and has published in prestigious journals such as Chemical Reviews, Journal of the American Chemical Society and Nature Communications.

In The Last Decade

Stefan Chmiela

20 papers receiving 3.1k citations

Hit Papers

Quantum-chemical insights from deep tensor neural networks 2017 2026 2020 2023 2017 2017 2021 2021 2023 250 500 750
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. Quantum-chemical insights from deep tensor neural networks
    2017 967 citations Kristof T. Schütt, Stefan Chmiela et al. Nature Communications profile →
  2. Machine learning of accurate energy-conserving molecular force fields
    2017 810 citations Stefan Chmiela, Alexandre Tkatchenko et al. Science Advances profile →
  3. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
    2021 546 citations John A. Keith, Valentín Vassilev-Galindo et al. Chemical Reviews profile →
  4. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
    2021 215 citations Oliver T. Unke, Stefan Chmiela et al. Nature Communications profile →
  5. Accurate global machine learning force fields for molecules with hundreds of atoms
    2023 112 citations Stefan Chmiela, Valentín Vassilev-Galindo et al. Science Advances profile →

Peers

Stefan Chmiela
Huziel E. Sauceda Mexico
Nicholas Lubbers United States
Raghunathan Ramakrishnan India
Michael Gastegger Germany
Kipton Barros United States
Katja Hansen Germany
Benjamin Nebgen United States
Kristof T. Schütt Germany
Justin S. Smith United States
Philippe Schwaller Switzerland
Huziel E. Sauceda Mexico
Stefan Chmiela
Citations per year, relative to Stefan Chmiela Stefan Chmiela (= 1×) peers Huziel E. Sauceda

Countries citing papers authored by Stefan Chmiela

Since Specialization
Citations

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

Fields of papers citing papers by Stefan Chmiela

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stefan Chmiela

This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Chmiela. A scholar is included among the top collaborators of Stefan Chmiela 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 Stefan Chmiela. Stefan Chmiela 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.
Chmiela, Stefan, et al.. (2025). Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach. Chemical Physics Letters. 867. 141964–141964. 1 indexed citations
2.
Кокорин, А. И., Huziel E. Sauceda, Stefan Chmiela, et al.. (2025). Atomic orbits in molecules and materials for improving machine learning force fields. Machine Learning Science and Technology. 6(3). 35005–35005. 1 indexed citations
3.
Kabylda, Adil, Leonardo Medrano Sandonas, Oliver T. Unke, et al.. (2025). Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields. Journal of the American Chemical Society. 147(37). 33723–33734. 13 indexed citations
4.
Unke, Oliver T., et al.. (2024). A Euclidean transformer for fast and stable machine learned force fields. Nature Communications. 15(1). 6539–6539. 41 indexed citations
5.
Müller, Klaus‐Robert, et al.. (2023). Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence. Journal of Chemical Theory and Computation. 19(14). 4619–4630. 1 indexed citations
6.
Chmiela, Stefan, Valentín Vassilev-Galindo, Oliver T. Unke, et al.. (2023). Accurate global machine learning force fields for molecules with hundreds of atoms. Science Advances. 9(2). eadf0873–eadf0873. 112 indexed citations breakdown →
7.
Kabylda, Adil, Valentín Vassilev-Galindo, Stefan Chmiela, Igor Poltavsky, & Alexandre Tkatchenko. (2023). Efficient interatomic descriptors for accurate machine learning force fields of extended molecules. Nature Communications. 14(1). 3562–3562. 19 indexed citations
8.
Müller, Klaus‐Robert, et al.. (2022). Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields. The Journal of Physical Chemistry Letters. 13(43). 10183–10189. 11 indexed citations
9.
Sauceda, Huziel E., et al.. (2022). BIGDML—Towards accurate quantum machine learning force fields for materials. Nature Communications. 13(1). 3733–3733. 54 indexed citations
10.
Unke, Oliver T., Stefan Chmiela, Michael Gastegger, et al.. (2021). SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nature Communications. 12(1). 7273–7273. 215 indexed citations breakdown →
11.
Keith, John A., Valentín Vassilev-Galindo, Bingqing Cheng, et al.. (2021). Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chemical Reviews. 121(16). 9816–9872. 546 indexed citations breakdown →
12.
Sauceda, Huziel E., Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, & Alexandre Tkatchenko. (2020). Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields. The Journal of Chemical Physics. 153(12). 124109–124109. 34 indexed citations
13.
Wang, Jiang, Stefan Chmiela, Klaus‐Robert Müller, Frank Noé, & Cecilia Clementi. (2020). Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. The Journal of Chemical Physics. 152(19). 38 indexed citations
14.
Schütt, Kristof T., et al.. (2020). Machine Learning Meets Quantum Physics. Lecture notes in physics. 100 indexed citations
15.
Sauceda, Huziel E., Stefan Chmiela, Igor Poltavsky, Klaus‐Robert Müller, & Alexandre Tkatchenko. (2019). Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces. Bulletin of the American Physical Society. 2019. 1 indexed citations
16.
Chmiela, Stefan, Huziel E. Sauceda, Igor Poltavsky, Klaus‐Robert Müller, & Alexandre Tkatchenko. (2019). sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Computer Physics Communications. 240. 38–45. 160 indexed citations
17.
Chmiela, Stefan, Huziel E. Sauceda, Klaus‐Robert Müller, & Alexandre Tkatchenko. (2018). Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields. RePEc: Research Papers in Economics. 2018. 2 indexed citations
18.
Schütt, Kristof T., et al.. (2017). Quantum-chemical insights from deep tensor neural networks. Nature Communications. 8(1). 13890–13890. 967 indexed citations breakdown →
19.
Chmiela, Stefan, Alexandre Tkatchenko, Huziel E. Sauceda, et al.. (2017). Machine learning of accurate energy-conserving molecular force fields. Science Advances. 3(5). e1603015–e1603015. 810 indexed citations breakdown →
20.
Chmiela, Stefan, et al.. (2016). Machine Learning of Accurate Energy-Conserving Molecular Force Fields. DepositOnce. 2017. 2 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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