Skip to main content Skip to navigation

Prof. James Kermode

Academic profile photograph

Prof. James Kermode

Professor of Materials Modelling

J dot R dot Kermode at warwick dot ac dot uk
+44 (0) 24 7652 8614


I am Professor of Materials Modelling in the School of Engineering at the University of Warwick, where I direct the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys) and the Warwick Centre for Predictive Modelling (WCPM).

Research Interests

Stress concentration at crack tipI develop multiscale materials modelling algorithms and the software that implements them, with a particular focus on machine learning and data-driven approaches, and on quantifying the uncertainty in the output of electronic structure and atomistic models. I am also active in applying parameter-free modelling techniques to make quantitative predictions of "chemomechanical" materials failure processes where stress and chemistry are tightly coupled, e.g. near the tip of a propagating crack (left), where local bond-breaking chemistry is driven by long-range stress fields. This 10-minute video provides an accessible overview of my research aimed at high-school and undergraduate students. Prominent examples include:

  • QM-accurate modelling of dislocations. Combining a quantum mechanical description of dislocation cores with an interatomic potential to capture long-range elastic relaxation allowed us to investigate plasticity in nickel-based superalloys and interactions between dislocations in tungsten with plasma components such as hydrogen (upper right, with tungsten atoms red, green and blue, hydrogen impurity purple and dislocation core and glide path red).
  • Machine Learning a General Purpose Interatomic Potential for Silicon. Albert Bartók-Pártay, Noam Bernstein and Gábor Csányi and I created a data-driven potential for silicon using the Gaussian approximation potential (GAP) framework. Our model, published in Physical Review X, is capable of accurately describing its behaviour across a wide range of temperature and pressures, and it comes along with uncertainty estimates that help estimate where it risks straying outside its domain of applicability (right, near a crack tip on the (111) cleavage plane, where uncertainty highest on red atoms).
  • Slow Crack Growth in Brittle Crystals. Everyday experience suggests that when brittle materials like glass or silicon wafers break they crack very fast, usually at a large fraction of the speed of sound in the material. In research published in Physical Review Letters, we presented QM-based atomistic simulations together with experimental results that overturn this intuition, showing how cracks in silicon can propagate very slowly via intrinsically 3D kink mechanisms.
  • Molecular Dynamics with On-the-fly Machine Learning of Quantum Mechanical Forces. In an article published in Physical Review Letters, we reported a new molecular dynamics scheme which combines first-principles molecular dynamics and machine-learning (ML) techniques in an information-efficient approach.
  • Scattering of cracks by individual atomic-scale impurities. In an article published in Nature Communications, we showed that a single atomic defect (e.g. a boron dopant, coloured orange in movie, above right) can be enough to deflect a crack as it travels through a crystal, leading to macroscopically observable surface features (lower right).

interactions between dislocation and impurities in tungsten

GAP uncertainty near silicon crack tip

Crack deflection at impurities

Teaching Interests

In 2023/24 I am teaching PX914 - Predictive Modelling and Uncertainty Quantification in the HetSys CDT and ES98E - Scientific Machine Learning in the MSc in Predictive Modelling and Scientific Computing.

Selected Publications

  1. P. Grigorev, A. M. Goryaeva, M.-C. Marinica, J. R. Kermode, and T. D. Swinburne,
    Calculation of Dislocation Binding to Helium-Vacancy Defects in Tungsten Using Hybrid Ab Initio-Machine Learning Methods, Acta Mater. 247 118734 (2023) [arXiv:2111.11262]
  2. L. Zhang, B. Onat, G. Dusson, A. McSloy, G. Anand, R. J. Maurer, C. Ortner, and J. R. Kermode, Equivariant Analytical Mapping of First Principles Hamiltonians to Accurate and Transferable Materials Models, npj Comp. Mater. 8, 158 (2022) [arXiv:2111.13736]
  3. S. Makri, C. Ortner and J. R. Kermode, A preconditioning scheme for Minimum Energy Path finding methods, J. Chem. Phys 150, 094109 (2019) [arXiv:1810.02705]
  4. A. P. Bartók, J. R. Kermode, N. Bernstein, and G. Csányi, Machine Learning a General-Purpose Interatomic Potential for Silicon, Phys. Rev. X 8, 041048 (2018)
  5. G. Sernicola, T. Giovannini, P. Patel, J. R. Kermode, D. S. Balint, T. Ben Britton, and F. Giuliani, In situ stable crack growth at the micron scale, Nat. Commun. 8, 108 (2017)
  6. J. R. Kermode, A. Gleizer, G. Kovel, L. Pastewka, G. Csányi, D Sherman and A De Vita, Low speed crack propagation via kink formation and advance on the silicon (110) cleavage plane,
    Phys. Rev. Lett, 115 135501 (2015)
  7. Z. Li, J. R. Kermode, and A. De Vita, Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces, Phys. Rev. Lett. 114, 096405 (2015)
  8. A. Gleizer, G. Peralta, J. R. Kermode, A. De Vita and D. Sherman, Dissociative Chemisorption of O2 Inducing Stress Corrosion Cracking in Silicon Crystals. Phys. Rev. Lett. 112, 115501 (2014)
  9. J. R. Kermode, L. Ben-Bashat, F. Atrash, J.J. Cilliers, D. Sherman and A. De Vita, Macroscopic scattering of cracks initiated at single impurity atoms. Nat. Commun. 4, 2441 (2013)
  10. J. R. Kermode, T. Albaret, D. Sherman, N. Bernstein, P. Gumbsch, M. C. Payne, G. Csányi and A. De Vita,
    Low speed fracture instabilities in a brittle crystal, Nature 455, 1224-1227 (2008)

See also my full Publications page, my Talks page, and my profiles on ORCID, Google Scholar and the Warwick Research Archive Portal. My PhD Thesis is available from the University of Cambridge's repository.

Projects and Grants

Previous Research Projects


No vacancies at present. HetSys CDT PhD projects for Oct 23 start will be advertised in late autumn 2022.


  • I’m one of the authors of the libAtoms/QUIP molecular dynamics framework
  • matscipy, a Python software library for computational materials science developed with Lars Pastewka.
  • f90wrap, a utility for wrapping Fortran 95 code to make it accessible from Python, including support for derived types.
  • Other software is available from my GitHub page, including an enhanced version of the AtomEye atomistic visualisation software which can read Extended XYZ (.xyz) and NetCDF (.nc) files (Note: the extended XYZ format is now also supported directly by OVITO and ASE).

External Collaborators