James R. Kermode

8.6k total citations · 4 hit papers
54 papers, 2.9k citations indexed

About

James R. Kermode is a scholar working on Materials Chemistry, Biomedical Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, James R. Kermode has authored 54 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Materials Chemistry, 12 papers in Biomedical Engineering and 11 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in James R. Kermode's work include Machine Learning in Materials Science (24 papers), X-ray Diffraction in Crystallography (9 papers) and Computational Drug Discovery Methods (8 papers). James R. Kermode is often cited by papers focused on Machine Learning in Materials Science (24 papers), X-ray Diffraction in Crystallography (9 papers) and Computational Drug Discovery Methods (8 papers). James R. Kermode collaborates with scholars based in United Kingdom, United States and France. James R. Kermode's co-authors include Noam Bernstein, Alessandro De Vita, Gábor Cśanyi, Albert P. Bartók, Zhenwei Li, Gábor Csányi, Carl Poelking, Michele Ceriotti, Sandip De and Peter Gumbsch and has published in prestigious journals such as Nature, Physical Review Letters and Circulation.

In The Last Decade

James R. Kermode

54 papers receiving 2.9k 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.

Machine learning unifies the modeling of materials and molecules

2017 495 citations Albert P. Bartók, Noam Bernstein et al. profile →
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

2015 437 citations James R. Kermode, Alessandro De Vita et al. Physical Review Letters profile →
Machine Learning a General-Purpose Interatomic Potential for Silicon

2018 349 citations Albert P. Bartók, James R. Kermode et al. profile →
Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum

2018 337 citations Olga Barrera, David Bombač et al. Journal of Materials Science profile →

Peers

James R. Kermode

AMPO

CTM

Alexander V. Shapeev Russia

Garritt J. Tucker United States

Ralf Drautz Germany

Shyam Dwaraknath United States

Francesca Tavazza United States

Nongnuch Artrith United States

Jutta Rogal Germany

Ellad B. Tadmor United States

Mitchell Wood United States

Franz Gähler Germany

Alexander V. Shapeev Russia

James R. Kermode

5.1k ×2.2

576 ×1.2

AMPO

618 ×1.3

CTM

902 ×2.1

153 ×0.4

Citations per year, relative to James R. Kermode James R. Kermode (= 1×) peers Alexander V. Shapeev

Countries citing papers authored by James R. Kermode

Since Specialization

Citations

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

Fields of papers citing papers by James R. Kermode

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James R. Kermode. 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 James R. Kermode. The network helps show where James R. Kermode may publish in the future.

Co-authorship network of co-authors of James R. Kermode

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

All Works

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20 of 20 papers shown

Kermode, James R., et al.. (2025). Bayesian selection for efficient MLIP dataset selection. Modelling and Simulation in Materials Science and Engineering. 33(5). 55020–55020. 1 indexed citations

Kermode, James R., et al.. (2024). A posteriori error estimate and adaptivity for QM/MM models of crystalline defects. Computer Methods in Applied Mechanics and Engineering. 428. 117097–117097. 1 indexed citations

Vasileiadis, Nikos, Craig White, Livio Gibelli, et al.. (2024). A DSMC-CFD coupling method using surrogate modelling for low-speed rarefied gas flows. Journal of Computational Physics. 520. 113500–113500. 4 indexed citations

Grigorev, Petr, Lucas Frérot, Andreas Klemenz, et al.. (2024). matscipy: materials science at the atomic scale withPython. The Journal of Open Source Software. 9(93). 5668–5668. 10 indexed citations

Sánchez, Ana M., et al.. (2024). Microcracks in CVD diamond produced by scaife polishing. Diamond and Related Materials. 144. 111008–111008. 2 indexed citations

Staunton, J. B., et al.. (2024). Collinear-spin machine learned interatomic potential for Fe7Cr2Ni alloy. Physical Review Materials. 8(3). 4 indexed citations

Grigorev, Petr, Alexandra M. Goryaeva, Mihai‐Cosmin Marinica, James R. Kermode, & Thomas D. Swinburne. (2023). Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods. Acta Materialia. 247. 118734–118734. 20 indexed citations

Witt, William C., Cas van der Oord, James P. Darby, et al.. (2023). ACEpotentials.jl: A Julia implementation of the atomic cluster expansion. The Journal of Chemical Physics. 159(16). 28 indexed citations

Kermode, James R., et al.. (2023). Massively parallel fitting of Gaussian approximation potentials. Machine Learning Science and Technology. 4(1). 15020–15020. 2 indexed citations

10.

Darby, James P., et al.. (2023). Gaussian approximation potentials: Theory, software implementation and application examples. The Journal of Chemical Physics. 159(17). 37 indexed citations

11.

Kermode, James R., et al.. (2021). Numerical-continuation-enhanced flexible boundary condition scheme applied to mode-I and mode-III fracture. Physical review. E. 103(3). 33002–33002. 8 indexed citations

12.

Kermode, James R.. (2020). f90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codes. Journal of Physics Condensed Matter. 32(30). 305901–305901. 41 indexed citations

13.

Barrera, Olga, David Bombač, Yi‐Sheng Chen, et al.. (2018). Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum. Journal of Materials Science. 53(9). 6251–6290. 337 indexed citations breakdown →

14.

Lambert, Henry, et al.. (2018). Imeall: A computational framework for the calculation of the atomistic properties of grain boundaries. Computer Physics Communications. 232. 256–263. 8 indexed citations

15.

Barrera, Olga, David Bombač, Yi‐Sheng Chen, et al.. (2018). Correction to: Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum. Journal of Materials Science. 53(14). 10593–10594. 12 indexed citations

16.

Sernicola, Giorgio, Tommaso Giovannini, James R. Kermode, et al.. (2017). In situ stable crack growth at the micron scale. Nature Communications. 8(1). 108–108. 61 indexed citations

17.

Kermode, James R., et al.. (2015). Low Speed Crack Propagation via Kink Formation and Advance on the Silicon (110) Cleavage Plane. Physical Review Letters. 115(13). 135501–135501. 28 indexed citations

18.

Li, Zhenwei, et al.. (2015). A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry. 115(16). 1129–1139. 24 indexed citations

19.

Kermode, James R., et al.. (2013). Macroscopic scattering of cracks initiated at single impurity atoms. Nature Communications. 4(1). 2441–2441. 45 indexed citations

20.

Cśanyi, Gábor, Gianpietro Moras, James R. Kermode, et al.. (2007). Multiscale modeling of defects in semiconductors : a novel molecular-dynamics scheme. Research Portal (King's College London). 104. 193–212. 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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