Dávid Péter Kovács

1.9k total citations · 3 hit papers
10 papers, 687 citations indexed

About

Dávid Péter Kovács is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Dávid Péter Kovács has authored 10 papers receiving a total of 687 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Materials Chemistry, 6 papers in Computational Theory and Mathematics and 3 papers in Molecular Biology. Recurrent topics in Dávid Péter Kovács's work include Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers) and Protein Structure and Dynamics (3 papers). Dávid Péter Kovács is often cited by papers focused on Machine Learning in Materials Science (7 papers), Computational Drug Discovery Methods (6 papers) and Protein Structure and Dynamics (3 papers). Dávid Péter Kovács collaborates with scholars based in United Kingdom, Canada and United States. Dávid Péter Kovács's co-authors include Gábor Cśanyi, Christoph Ortner, Maxwell J. Robb, Ilyes Batatia, Cas van der Oord, Alpha A. Lee, Daniel J. Cole, Venkat Kapil, Alice E. A. Allen and Angelos Michaelides and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Nature Communications.

In The Last Decade

Dávid Péter Kovács

10 papers receiving 676 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.

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

2023 103 citations Dávid Péter Kovács, Ilyes Batatia et al. The Journal of Chemical Physics profile →
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 →
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules

2025 48 citations Dávid Péter Kovács, J. Harry Moore et al. Journal of the American Chemical Society profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dávid Péter Kovács United Kingdom	9	506	188	167	100	79	10	687
Piero Gasparotto Switzerland	9	394 0.8×	164 0.9×	94 0.6×	127 1.3×	50 0.6×	15	595
Maksim Kulichenko United States	17	458 0.9×	101 0.5×	101 0.6×	68 0.7×	75 0.9×	28	689
Valentín Vassilev-Galindo Luxembourg	10	661 1.3×	131 0.7×	276 1.7×	187 1.9×	91 1.2×	12	1.0k
Simon Axelrod United States	12	387 0.8×	85 0.5×	213 1.3×	219 2.2×	55 0.7×	17	654
Martin Stöhr Luxembourg	9	231 0.5×	176 0.9×	72 0.4×	61 0.6×	70 0.9×	10	426
Jinxiao Zhang China	12	278 0.5×	77 0.4×	94 0.6×	87 0.9×	78 1.0×	29	481
Max Pinheiro Brazil	13	327 0.6×	262 1.4×	112 0.7×	79 0.8×	72 0.9×	30	605
Julia Westermayr Germany	10	263 0.5×	130 0.7×	107 0.6×	76 0.8×	47 0.6×	19	371
Efthymios I. Ioannidis United States	7	285 0.6×	92 0.5×	102 0.6×	75 0.8×	49 0.6×	7	401
Raimondas Galvelis United Kingdom	15	636 1.3×	67 0.4×	152 0.9×	195 1.9×	101 1.3×	18	1.0k

Countries citing papers authored by Dávid Péter Kovács

Since Specialization

Citations

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

Fields of papers citing papers by Dávid Péter Kovács

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dávid Péter Kovács. 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 Dávid Péter Kovács. The network helps show where Dávid Péter Kovács may publish in the future.

Co-authorship network of co-authors of Dávid Péter Kovács

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

All Works

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10 of 10 papers shown

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 →

Kovács, Dávid Péter, J. Harry Moore, Nicholas J. Browning, et al.. (2025). MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules. Journal of the American Chemical Society. 147(21). 17598–17611. 48 indexed citations breakdown →

Darby, James P., Dávid Péter Kovács, Ilyes Batatia, et al.. (2023). Tensor-Reduced Atomic Density Representations. Physical Review Letters. 131(2). 28001–28001. 26 indexed citations

Kovács, Dávid Péter, et al.. (2023). Evaluation of the MACE force field architecture: From medicinal chemistry to materials science. The Journal of Chemical Physics. 159(4). 103 indexed citations breakdown →

Oord, Cas van der, et al.. (2023). Hyperactive learning for data-driven interatomic potentials. npj Computational Materials. 9(1). 168–168. 69 indexed citations

Kapil, Venkat, Dávid Péter Kovács, Gábor Cśanyi, & Angelos Michaelides. (2023). First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday Discussions. 249(0). 50–68. 40 indexed citations

Kovács, Dávid Péter, et al.. (2022). MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. 11423–11436. 3 indexed citations

Kovács, Dávid Péter, et al.. (2021). Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias. Nature Communications. 12(1). 1695–1695. 70 indexed citations

Kovács, Dávid Péter, Cas van der Oord, Alice E. A. Allen, et al.. (2021). Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE. Journal of Chemical Theory and Computation. 17(12). 7696–7711. 93 indexed citations

10.

Kovács, Dávid Péter, et al.. (2020). Validation of the CoGEF Method as a Predictive Tool for Polymer Mechanochemistry. Journal of the American Chemical Society. 142(38). 16364–16381. 174 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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