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School of Engineering

Dr James Kermode

Reader

MA MSci PhD MInstP FHEA

I am a Reader in the School of Engineering at the University of Warwick, where I am also associated with the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys; Co-director) and the Warwick Centre for Predictive Modelling (WCPM; Co-director).

June 2019-present Reader, School of Engineering, University of Warwick
2016-2019 Associate Professor, School of Engineering, University of Warwick
2014-2016 Assistant Professor, School of Engineering, University of Warwick 
2009-2014 Postdoc in the Department of Physics at King's College London
2007-2008 Postdoc in the Department of Engineering at the University of Cambridge
2004-2007 PhD in the TCM Group at the Cavendish Laboratory, University of Cambridge

Teaching

In 2020/21 I am teaching ES386 - Dynamics of Vibrating Systems (with Dr Peter Brommer) and PX914 - Predictive Modelling and Uncertainty Quantification in the HetSys CDT.

Previously I was module leader for IL027 Interdisciplinary Computer Modelling (with Prof. Christoph Ortner).

My office hours are Fridays 3-4pm during term.

Vacancies

News

Research Interests

Stress concentration at crack tipI develop multiscale materials modelling algorithms and the software that implements them. My recent work applies this parameter-free modelling 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. Recents projects include:

  • 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 new experimental results from collaborators at the Technion in Israel 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 report a new molecular dynamics scheme which combines first-principles molecular dynamics and machine-learning (ML) techniques in a single information-efficient approach. The scheme works by going “shopping” in a database of reference configurations whenever QM forces are required. If there’s something similar enough, it interpolates using Bayesian inference to predict forces. If not, a new QM calculation is carried out on the fly to extend the database (Read more).
  • 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 (centre right).
  • Stress corrosion crack propagation in a chemically aggressive environment. Our joint experimental/theoretical work published in Physical Review Letters showed that oxygen molecules (coloured red, below right) arriving at a crack tip dissociate spontaneously, releasing enough energy to "burn" individual silicon bonds. This work benefited from access to the Argonne Leadership Computing Facility.
  • Low speed fracture instabilities. In an earlier work published in Nature, we discovered that the (111) cleavage plane in silicon becomes unstable at speeds below about 1000 m/s. The instability arises from an atomic scale reconstruction of the crack tip which can only be accurately modelled with quantum mechanical precision.

Machine Learning On the Fly

Crack deflection at impurities

silicon-scc-2.png

Selected Publications

  1. B. Onat, C. Ortner and J. R. Kermode, Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials, J. Chem. Phys. 153, 144106 (2020) [arXiv:2006.01905] [Open Access]
  2. P. Grigorev, T. Swinburne and J. R. Kermode, QM/MM study of hydrogen-decorated screw dislocations in tungsten: ultrafast pipe diffusion, core reconstruction and effects on glide mechanism,
    Phys. Rev. Materials 4 023601 (2020) [Open access]
  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) [arXiv] [Open Access]
  5. A. P. Bartok, S. De, C. Poelking, N. Bernstein, J. R. Kermode, G. Csányi and M. Ceriotti, Machine learning unifies the modeling of materials and molecules. Science Advances 3, e1701816 (2017). [arXiv] [Open Access]
  6. T. D. Swinburne and J. R. Kermode, Computing energy barriers for rare events from hybrid quantum/classical simulations through the virtual work principle, Phys. Rev. B 96, 144102 (2017). [arXiv] [Open Access]
  7. 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). [Open Access]
  8. F. Bianchini, J.R. Kermode, and A. De Vita, Modelling defects in Ni–Al with EAM and DFT calculations, Modell. Simul. Mater. Sci. Eng. 24, 045012 (2016) [Open Access]
  9. D. Packwood, J. R. Kermode, L. Mones, N. Bernstein, J. Woolley, N. Gould, C. Ortner, and G. Csányi, A universal preconditioner for simulating condensed phase materials, J. Chem. Phys. 144, 164109 (2016).
    [arXiv] [Open Access]
  10. M. Aldegunde, J. R. Kermode, and N. Zabaras, Development of an exchange–correlation functional with uncertainty quantification capabilities for density functional theory, J. Comput. Phys. 311, 173 (2016).
  11. 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) [Open Access]
  12. 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) [Open Access]
  13. E. Bitzek, J. R. Kermode and P. Gumbsch, Atomistic aspects of fracture, Int. J. Fract. 191, 13-30 (2015)
    [Open Access]
  14. 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).
  15. 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).
  16. N. Bernstein, J. R. Kermode and G. Csányi, Hybrid atomistic simulation methods for materials systems. Rep. Prog. Phys. 72, 026501 (2009).
  17. 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.

Current Research Projects

Previous Research Projects

Software

External Collaborators

James Kermode

Warwick Centre
for Predictive Modelling
School of Engineering
University of Warwick
Coventry
CV4 7AL
United Kingdom

Office: D210
Phone: +44 (0) 24 765 28614
E-mail: j.r.kermode@warwick.ac.uk