Using computers to understand the atomic structure of materials


UQ-aware sampling at scale
Bayesian control of supercomputers to build models we can trust

Vibrational thermodynamics
Going beyond harmonic theories to better match simulation and experiment

Hybrid DFT/ML-MD methods
Extending the capability of ab initio material simulations

The design of more durable materials is key to reduce the carbon emissions of construction, transportation and energy production. To achieve this requires an understanding of mechanical strength and toughness at the level of jiggling atoms.

We design theoretical and numerical tools to discover the atomic mechanisms by which solid materials bend and break. As the range of possibilities is practically infinite, the challenge is to find the most influential, then characterize these accurately. A particular focus is designing autonomous methods that harness massively parallel computers to rapidly search configurational space. This requires the development of statistical uncertainty measures, coarse-grained modelling tools and data-efficient machine learning approaches.

A better idea of our research interests can be found in our publications and open source software.


  Recent Publications

= corresponding author(s). Full list at Google Scholar.


  • Tom Swinburne
  • me 18-:  CNRS researcher (CRCN), CINaM

    Curriculum Vitae
  • Petr Grigorev
  • me 20-23:  Postdoc, UQ-aware sampling and hybrid methods
    Funded by ANR MEMOPAS (PI: TDS)

    Personal Website
  • Clovis Lapointe
  • 18-22:  PhD Student, machine learning of point defect entropics
    Joint Supervision with Dr MC Marinica, CEA Saclay
  • Raynol Dsouza
  • 18-22:  PhD Student, mean field models for lattice vibrations
    External Supervision with Dr L Huber and Prof J Neugebauer, MPIE Dusseldorf

    ------------------ Past Members ------------------

  • Deepti Kannan
  • 20-21:  MSc Student / Marshall Scholar, coarse graining of Markov Models
    Joint Supervision with Prof DJ Wales, University of Cambridge



  • ANR MeMoPas (2020-2022)
  • PI on ANR JCJC project, Mesoscale models from massively parallel atomistic simulations: uncertainty driven, self-optimizing strategies for hard materials


  • EuroFusion / IREMEV program
  • Computational resources (2020-2021) for studies of timescale estimation of microstructural evolution


  • GENCI / IDRIS national supercomputing centers
  • Computational resources (2019-2020,2020-2021) for high-throughput studies of defect diffusion mechanisms

      Job Openings

    If you are interested in masters projects at AMU, or are considering an independent funding application, please email me.

    We currently have no open contracts, but are always open to applications from motivated postdocs or PhD candidates. Please email me to discuss funding possibilities.

      Some Presentations

  • Kink limited motion of line defects: multiscale simulation and analysis
  • Applied analysis seminar, Imperial College London, February 2019 (Invited)

  • Kink limited Orowan strengthening and the brittle to ductile transition of bcc metals
  • Oxford MFFP Meeting, September 2018 (Invited)

  • Uncertainty-driven construction of Markov models from accelerated molecular dynamics
  • Advances in Computational Statistical Physics, CIRM, France, September 2018 (Invited)

  • Fast, vacancy-free climb of dislocation loops in bcc metals
  • NUMAT conference, Montpellier, France, 2016

  • Multiscale analysis of nonlinear dislocation models
  • MMM conference, Berkeley, USA, 2014


       swinburne cinam "." univ-mrs "." fr

       CINaM, Campus de Luminy, 13288 Marseille, FRANCE