Designing more durable materials is key to reduce carbon emissions
of construction, transportation and energy production. This requires
understanding mechanical strength and toughness at the level of
jiggling atoms.
We design atomic simulation methods to discover how metals bend and break.
The range of atomic deformation mechanisms is vast- the challenge is to discover and characterize the most influential.
A short summary is below; for more detail please see our publications and open source software.
News
Main Research Themes
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Autonomous exploration at scale: TAMMBER
TAMMBER uses Bayesian methods to build kMC/Markov models
with robust UQ on sampling incompleteness.
We use this UQ to optimize resourse allocation in
parallel computation, and bound predictions of diffusion models.
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ML-assisted QM/MM simulations
We use retrained ML potentials to embed DFT simulations in
large-scale MD with seamless
coupling. This permits DFT calculations at unprecedented scales.
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Vibrational free energies beyond HTST: PAFI
PAFI is a path-constrained sampling routine, giving exact vibrational
free energy differences for linear-scaling effort. This enables evaluation of
activation free energies for very large systems
which can not be treated by HTST approximations.
TDS† and M-C Marinica, PRL (2018)
PAFI |
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Surrogate ML models for defect entropics
(PhD of Clovis Lapointe, with M-C Marinica) |
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Analysis of Markov Models: PyGT
Much atomic data is a large network of metastable states. The Markov
dynamics cannot be treated by usual routines (LAPACK) due to conditioning issues;
PyGT provides a solution for these challenging networks.
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Mean-field models for lattice vibrations: BLaSa
Analytic mean-field approach for local anharmonic models, evaluating
anharmonic vibrational phase free energies for
milliseconds of effort.
Current developments are within the PhD of R Dsouza since 2021. |
Team
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10/18-: CNRS researcher, CINaM 04/17-06/18: Postdoc, T-1, LANL 03/15-03/17: EuroFusion fellow, CCFE 10/11-03/15: MSc+PhD, Imperial College London Curriculum Vitae |
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20-23: Postdoc, Machine learning assisted DFT methods Funded by ANR MEMOPAS (PI: TDS) 2024- CNRS researcher Personal Website |
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09/20-09/23: PhD, mean field vibration models. External with Prof J Neugebauer, MPIE Dusseldorf | |
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-03/23: PhD, anharmonic effects in plasticity. External with Prof D Moldovan, Louisiana State University, USA |
Past Members
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18-22:
PhD, Machine learning defect entropics. With Dr MC Marinica, CEA Saclay
now: Postdoc, U Lorraine |
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20-21:
MSc, reducing Markov Chains. With Prof DJ Wales, Cambridge.
now: PhD, MIT |
Main Collaborators
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Analysis and convergence of highly metastable systems; Markov models e.g. JCTC 2020 PyGT |
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Linear machine learning models, free energy calculation e.g. PRMat 2020 PRL 2018 PAFI |
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Massively parallel sampling of metastable systems e.g. NPJ Comp. Mat 2020 TAMMBER |
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Hybrid DFT/empirical methods e.g. PRB 2017 arXiv 2021/2 LML-retrain |
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Mean field models for anharmonic lattice vibrations PRB RC 2020 BLaSa |
Recent Publications
† = corresponding author(s). Full list at Google Scholar.Software
- PAFI- Projected Average Force Integrator
- Anharmonic free energy barrier evaluations using in LAMMPS/MPI/C++
Physical Review Letters 2018
Works using PAFI (more coming soon...) Y Sato et al., D Rodney MRL (2021)
R Namakian et al. Computational Material Science (2022, accepted) - TAMMBER- Temperature Accelerated Markov Models, Bayesian Estimation of Rates
- Massively parallel, autonmous exploration of atomic systems
Physical Review Materials 2018
TDS† and D Perez, NPJ Computational Materials (2020)
Works using TAMMBER (more coming soon...) K Ferasat et al., Materialia (2021) - PyGT- Graph Transformation in Python
- Stable analysis of metastable Markov chains
J Chem. Th. Comp. 2020
Works using PyGT (more coming soon...) TDS et al. JCP (2020)
D Kannan et al. JCP (2020)
E Woods et al. Proc. Roy. Soc. (2022, accepted)
- BLaSa- Bond Lattice Sampling and Analytics
- Execution and analysis of bond lattice dynamics
Physical Review B Rapid Communications 2020
Works using BLaSa (coming soon!) PhD of Raynol Dsouza (MPIE Dusseldorf)
Funding
- CEA PTC ANEMONE (2022-2024)
Co-PI with Dr L Ventelon, CEA Saclay, developing ML-assisted DFT for ab initio accuracy in large scale simulations of extended defects.
- 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 (2020-2022) for studies of timescale estimation of microstructural evolution
- GENCI / IDRIS national supercomputing centers
Computational resources (2019-2023) for multiple studies of defect diffusion and plasticity mechanisms




Job Openings
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CNRS "Emergence" Postdoc: Automatic Differentiation of Energy Landscapes
We currently have an opening for a 12 month postdoctoral project concerning
uncertainty quantification of diffusion models, which will use emerging automatic
differentation tools. Start date October 2023.
If this sounds interesting, please email me with a CV and motivation letter. - If you are interested in masters or docotral projects at funded by the AMU doctoral school, please email me.
Some Presentations
- Kink limited motion of line defects: multiscale simulation and analysis
- Applied analysis seminar, Imperial College London, February 2019 (Invited) Slides
- Kink limited Orowan strengthening and the brittle to ductile transition of bcc metals
- Oxford MFFP Meeting, September 2018 (Invited) Slides
- Uncertainty-driven construction of Markov models from accelerated molecular dynamics
- Advances in Computational Statistical Physics, CIRM, France, September 2018 (Invited) Slides
- Fast, vacancy-free climb of dislocation loops in bcc metals
- NUMAT conference, Montpellier, France, 2016 Slides
- Multiscale analysis of nonlinear dislocation models
- MMM conference, Berkeley, USA, 2014 Slides