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School of Engineering

Publications

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

2022

  1. James P Darby, James R Kermode, and Gábor Csányi. Compressing local atomic neighbourhood descriptors. npj Computational Materials, 8:166, August 2022. doi:10.1038/s41524-022-00847-y.
  2. Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G Anand, Reinhard J Maurer, Christoph Ortner, and James R Kermode. Equivariant analytical mapping of first principles hamiltonians to accurate and transferable materials models. npj Computational Materials, 8:158, July 2022. doi:10.1038/s41524-022-00843-2.

2021

  1. V Podgurschi, D J M King, J Smutna, J R Kermode, and M R Wenman. Atomistic modelling of iodine-oxygen interactions in strained sub-oxides of zirconium. J. Nucl. Mater., pages 153394, November 2021. doi:10.1016/j.jnucmat.2021.153394.
  2. Alexandra M Goryaeva, Julien Dérès, Clovis Lapointe, Petr Grigorev, Thomas D Swinburne, James R Kermode, Lisa Ventelon, Jacopo Baima, and Mihai-Cosmin Marinica. Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc fe and W. Phys. Rev. Materials, 5(10):103803, October 2021. doi:10.1103/PhysRevMaterials.5.103803.
  3. Maciej Buze and James R Kermode. Numerical-continuation-enhanced flexible boundary condition scheme applied to mode-i and mode-III fracture. Phys. Rev. E, 103(3):033002, March 2021. doi:10.1103/PhysRevE.103.033002.
  4. S Mostafa Khosrownejad, James R Kermode, and Lars Pastewka. Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations. Phys. Rev. Materials, 5(2):023602, February 2021. doi:10.1103/PhysRevMaterials.5.023602.

2020

  1. Berk Onat, Christoph Ortner, and James R Kermode. Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials. J. Chem. Phys., 153(14):144106, October 2020. doi:10.1063/5.0016005.
  2. Jacek R Gołębiowski, James R Kermode, Peter D Haynes, and Arash A Mostofi. Atomistic QM/MM simulations of the strength of covalent interfaces in carbon nanotube-polymer composites. Phys. Chem. Chem. Phys., May 2020. doi:10.1039/d0cp01841d.
  3. James R Kermode. F90wrap: an automated tool for constructing deep python interfaces to modern fortran codes. J. Phys. Condens. Matter, March 2020. doi:10.1088/1361-648X/ab82d2.
  4. Petr Grigorev, Thomas D Swinburne, and James R Kermode. Hybrid quantum/classical study of hydrogen-decorated screw dislocations in tungsten: ultrafast pipe diffusion, core reconstruction, and effects on glide mechanism. Phys. Rev. Materials, 4(2):023601, February 2020. doi:10.1103/PhysRevMaterials.4.023601.

2019

  1. F Bianchini, A Glielmo, J R Kermode, and A De Vita. Enabling QM-accurate simulation of dislocation motion in \textbackslashgamma -ni and \textbackslashalpha -fe using a hybrid multiscale approach. Phys. Rev. Materials, 3(4):043605, April 2019. doi:10.1103/PhysRevMaterials.3.043605.
  2. Stela Makri, Christoph Ortner, and James R Kermode. A preconditioning scheme for minimum energy path finding methods. J. Chem. Phys., 150(9):094109, March 2019. doi:10.1063/1.5064465.

2018

  1. Albert P Bartók, James Kermode, Noam Bernstein, and Gábor Csányi. Machine learning a General-Purpose interatomic potential for silicon. Phys. Rev. X, 8(4):041048, December 2018. doi:10.1103/PhysRevX.8.041048.
  2. Jacek R Gołębiowski, James R Kermode, Arash A Mostofi, and Peter D Haynes. Multiscale simulations of critical interfacial failure in carbon nanotube-polymer composites. J. Chem. Phys., 149(22):224102, December 2018. doi:10.1063/1.5035508.
  3. David Stephenson, James R Kermode, and Duncan A Lockerby. Accelerating multiscale modelling of fluids with on-the-fly gaussian process regression. Microfluid. Nanofluidics, 22(12):139, November 2018. doi:10.1007/s10404-018-2164-z.
  4. H Lambert, Adam Fekete, J R Kermode, and A De Vita. Imeall: a computational framework for the calculation of the atomistic properties of grain boundaries. Comput. Phys. Commun., 232:256–263, November 2018. doi:10.1016/j.cpc.2018.04.029.
  5. O Barrera, D Bombac, Y Chen, T D Daff, E Galindo-Nava, P Gong, D Haley, R Horton, I Katzarov, J R Kermode, C Liverani, M Stopher, and F Sweeney. Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum. J. Mater. Sci., pages 1–40, February 2018. doi:10.1007/s10853-017-1978-5.

2017

  1. Albert P Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R Kermode, Gábor Csányi, and Michele Ceriotti. Machine learning unifies the modeling of materials and molecules. Science Advances, 3(12):e1701816, December 2017. doi:10.1126/sciadv.1701816.
  2. Thomas D Swinburne and James R Kermode. Computing energy barriers for rare events from hybrid quantum/classical simulations through the virtual work principle. Phys. Rev. B, 96:144102, 2017. doi:10.1103/PhysRevB.96.144102.
  3. Giorgio Sernicola, Tommaso Giovannini, Punit Patel, James R Kermode, Daniel S Balint, T Ben Britton, and Finn Giuliani. In situ stable crack growth at the micron scale. Nat. Commun., 8(1):108, July 2017. doi:10.1038/s41467-017-00139-w.
  4. Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E Castelli, Rune Christensen, Marcin Dułak, Jesper Friis, Michael N Groves, Bjørk Hammer, Cory Hargus, Eric D Hermes, Paul C Jennings, Peter Bjerre Jensen, James Kermode, John R Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian S Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W Jacobsen. The atomic simulation environment—a python library for working with atoms. J. Phys. Condens. Matter, 29(27):273002, June 2017. doi:10.1088/1361-648X/aa680e.
  5. Ask Larsen, Jens Mortensen, Jakob Blomqvist, Ivano Castelli, Rune Christensen, Marcin Dulak, Jesper Friis, Michael Groves, Bjork Hammer, Cory Hargus, Eric Hermes, Paul Jennings, Peter Jensen, James Kermode, John Kitchin, Esben Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jon Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten Wedel Jacobsen. The atomic simulation environment — a python library for working with atoms. J. Phys. Condens. Matter, March 2017. doi:10.1088/1361-648X/aa680e.

2016

  1. David Packwood, James Kermode, Letif Mones, Noam Bernstein, John Woolley, Nicholas Gould, Christoph Ortner, and Gábor Csányi. A universal preconditioner for simulating condensed phase materials. J. Chem. Phys., 144(16):164109, April 2016. doi:10.1063/1.4947024.
  2. 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(4):045012, April 2016. doi:10.1088/0965-0393/24/4/045012.
  3. David Packwood, James Kermode, Letif Mones, Noam Bernstein, John Woolley, Nicholas Gould, Christoph Ortner, and Gábor Csányi. A universal preconditioner for simulating condensed phase materials. J. Chem. Phys., 144(16):164109, April 2016. doi:10.1063/1.4947024.
  4. Manuel Aldegunde, James R Kermode, and Nicholas Zabaras. Development of an exchange–correlation functional with uncertainty quantification capabilities for density functional theory. J. Comput. Phys., 311:173–195, April 2016. doi:10.1016/j.jcp.2016.01.034.

2015

  1. G Corbett, J Kermode, D Jochym, and K Refson. A python interface to CASTEP. Rutherford Appleton Laboratory Technical Reports, 2015.
  2. Anke Peguiron, Lucio Columbi Ciacchi, Alessandro De Vita, James R Kermode, and Gianpietro Moras. Accuracy of buffered-force QM/MM simulations of silica. J. Chem. Phys., 142:064116, 2015. doi:10.1063/1.4907786.
  3. Zhenwei Li, James R Kermode, and Alessandro De Vita. Molecular dynamics with On-the-Fly machine learning of Quantum-Mechanical forces. Phys. Rev. Lett., 114(March):096405, 2015. doi:10.1103/PhysRevLett.114.096405.
  4. James R Kermode, Anna Gleizer, Guy Kovel, Lars Pastewka, Gábor Csányi, Dov Sherman, and Alessandro De Vita. Low speed crack propagation via kink formation and advance on the silicon (110) cleavage plane. Phys. Rev. Lett., 115(13):135501, September 2015. doi:10.1103/PhysRevLett.115.135501.
  5. Marco Caccin, Zhenwei Li, James R Kermode, and Alessandro De Vita. A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers. Int. J. Quantum Chem., 115(16):1129–1139, August 2015. doi:10.1002/qua.24952.
  6. Erik Bitzek, James R Kermode, and Peter Gumbsch. Atomistic aspects of fracture. Int. J. Fract., 191(1-2):13–30, February 2015. doi:10.1007/s10704-015-9988-2.

2014

  1. James Kermode, Giovanni Peralta, Zhenwei Li, and Alessandro De Vita. Multiscale modelling of materials chemomechanics: brittle fracture of oxides and semiconductors. Procedia Materials Science, 3:1681–1686, 2014. doi:10.1016/j.mspro.2014.06.271.
  2. Gaurav Singh, James R Kermode, Alessandro De Vita, and Robert W Zimmerman. Validity of linear elasticity in the crack-tip region of ideal brittle solids. Int. J. Fract., 189(1):103–110, July 2014. doi:10.1007/s10704-014-9958-0.
  3. Anna Gleizer, Giovanni Peralta, James R Kermode, Alessandro De Vita, and Dov Sherman. Dissociative chemisorption of O2 inducing stress corrosion cracking in silicon crystals. Phys. Rev. Lett., 112(11):115501, March 2014.

2013

  1. 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, September 2013. doi:10.1038/ncomms3441.

2010

  1. J R Kermode, S Cereda, P Tangney, and A De Vita. A first principles based polarizable O(N) interatomic force field for bulk silica. J. Chem. Phys., 133(9):094102, September 2010. doi:10.1063/1.3475565.

2009

  1. Noam Bernstein, J R Kermode, and G Csányi. Hybrid atomistic simulation methods for materials systems. Rep. Prog. Phys., 72(2):026501, 2009. doi:10.1088/0034-4885/72/2/026501.
  2. James Kermode, Steven Winfield, Gabor Csanyi, and Mike Payne. DFT embedding and coarse graining techniques. Multiscale Simul. Methods Mol. Sci, 42:215–228, 2009. doi:10.1.1.155.1153.

2008

  1. J R Kermode, T Albaret, Dov Sherman, Noam Bernstein, P Gumbsch, M C Payne, Gábor Csányi, and A De Vita. Low-speed fracture instabilities in a brittle crystal. Nature, 455(7217):1224–1227, October 2008. doi:10.1038/nature07297.

2007

  1. Gábor Csányi, Steven Winfield, J R Kermode, A De Vita, Alessio Comisso, Noam Bernstein, and Michael C Payne. Expressive programming for computational physics in fortran 95+. IoP Comput. Phys. Newsletter, pages Spring 2007, 2007.

2006

  1. Gábor Csányi, G Moras, J R Kermode, Michael C Payne, A Mainwood, and A De Vita. Theory of defects in semiconductors. Collections, 104:193–212, 2006. doi:10.1007/11690320.