TD Swinburne

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.

  Team

Tom Swinburne		18-:  CNRS researcher (CRCN), CINaM Curriculum Vitae
Petr Grigorev		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

  Funding

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.

  Contact

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

   CINaM, Campus de Luminy, 13288 Marseille, FRANCE