News
10/20 New paper accepted in NPJ Computational Materials
With Danny Perez, LANL. Unsupervised method to calculate defect diffusivities, with a rare convergence measure.
10/20 New paper in Journal of Chemical Physics
With Deepti Kannan, Daniel Sharpe and David Wales, U Cambridge. How first passage time distributions can be evaluated for highly metastable systems.
09/20 New paper in PRB Rapid Communications
Primarily with Jan Janssen, Mira Todorova and Joerg Neugebauer of MPIE Dusseldorf. Neat mean field model for highly anharmonic free energies.
07/20 Is your Markov chain too metastable to analyze? Try PyGT!
05/20 First paper by Clovis Lapointe in PRMat.
PhD student joint with MC Marinica. Machine learning prediction of defect formation entropy, with really impressive transferability. Well done Clovis!
03/20 New paper in Journal of Chemical Theory and Computation
With David J Wales on converging numerically challenging reaction networks, with application to nanoclusters.
02/20 I am coorganising COSIRES 2021 (NEW DATES DUE TO COVID19)
With Cosmin Marinica (CEA Saclay) and CĂ©line Varvenne (CNRS/CINaM) on the island of Porquerolles, France.
01/20 Upcoming invited talks:
WCCM 202021, SIAMMS2021 and MMM202021. Let me know if you'll be there.... next year.
CV / Bio
Thomas D Swinburne
Curriculum Vitae
18: Tenured junior CNRS researcher at CINaM Marseille
1718: Postdoc at Los Alamos with Danny Perez
1517: EuroFusion fellowship at CCFE with Prof S L Dudarev
1115: PhD at Imperial College London under Prof A P Sutton FRS
I develop theoretical and computational approaches to connect the messy, jiggling atoms of real materials to their mechanical and thermodynamic properties. Materials transform via complex, collective atomic processes, but the rate at which these processes occur varies wildly, from nanoseconds to months. It is often essential to understand these "rare event" kinetics to connect theory and simulation to experiment. This requires discovery of the atomistic mechanisms and a model for the dynamics at finite temperature, both of which can be challenging. Traditionally this is done using domain expertise and scientific intuition; a current focus is combining this with statistical measures of uncertainty to build autonomous methods for massively parallel computers.
Automated approaches are instrinsically compatible with the current explosion in machine learning approaches to understand materials, but I also develop analytic models (human learning...). Applications to date have focused on the design of safer reactors for nuclear fusion and fission, whilst current interests include nanoclusters for heterogeneous catalysis and diffusion in random alloys.
Recent Publications
† = corresponding author(s)
 Optimal dimensionality reduction of Markov chains using graph transformation
 D Kannan, DJ Sharpe, TD Swinburne† and DJ Wales†
Submitted, 2020  Statistical mechanics of kinks on a gliding screw dislocation
 M Boleininger†, M Gallauer, SL Dudarev, TD Swinburne, DR Mason and D Perez
Physical Review Research, Accepted, 2020  Automated calculation of defect transport tensors
 TD Swinburne† and D Perez
NPJ Computational Materials, Accepted, 2020  Anharmonic free energy of lattice vibrations in fcc crystals from a meanfield bond
 TD Swinburne†, J Janssen, M Todorova, G Simpson, P Plechac, M Luskin, and J Neugebauer
Physical Review B Rapid Communications, 2020  Rare Events and First Passage Time Statistics From the Energy Landscape
 TD Swinburne† D Kannan, DJ Sharpe and DJ Wales
Journal of Chemical Physics, 2020  Ultraviolet catastrophe of a fluctuating curved dislocation line
 M Boleininger†, TD Swinburne, L Dupuy and SL Dudarev
Physical Review Research 2020  Machine learning surrogate models for prediction of point defect vibrational entropy
 C Lapointe†, TD Swinburne†, L Thiry, S Mallat, L Proville, CS Becquart, MC Marinica†
Physical Review Materials 2020  Defining, calculating and converging observables of kinetic transition networks
 TD Swinburne† and DJ Wales
Journal of Chemical Theory and Computation 2020  Hybrid quantum/classical study of hydrogendecorated screw dislocations in tungsten: Ultrafast pipe diffusion, core reconstruction, and effects on glide mechanism
 P Grigorev†, TD Swinburne, JR Kermode†
Physical Review Materials 2020  Quantum detrapping and transport of heavy defects in tungsten
 K Arakawa†, MC Marinica, SP Fitzgerald, L Proville, D NguyenManh, SL Dudarev, PW Ma, TD Swinburne, AM Goryaeva, T Yamada, T Amino, S Arai,
Y Yamamoto, K Higuchi, N Tanaka, H Yasuda, T Yasuda, H Mori
Nature Materials 2020  Atomistictocontinuum description of edge dislocation core: Unification of the PeierlsNabarro model with linear elasticity
 M Boleininger†, TD Swinburne and SL Dudarev
Physical Review Materials 2018  Kinklimited Orowan strengthening explains the ductile to brittle transition of irradiated and unirradiated bcc metals
 TD Swinburne† and SL Dudarev
Physical Review Materials 2018 (Editor's Suggestion)  Selfoptimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification
 TD Swinburne† and D Perez
Physical Review Materials 2018  Unsupervised calculation of free energy barriers in large crystalline systems
 TD Swinburne† and MC Marinica
Physical Review Letters 2018  Computing energy barriers for rare events from hybrid quantum/classical simulations through the virtual work principle
 TD Swinburne† and J R Kermode
Physical Review B 2017  Low temperature diffusivity of selfinterstitial defects in tungsten
 TD Swinburne†, PW Ma and SL Dudarev
New Journal Physics 2017  Fast, vacancyfree climb of dislocation loops in bcc metals
 TD Swinburne†, K Arakawa, H Mori, H Yasuda, M Isshiki, K Mimura, M Uchikoshi and SL Dudarev
Scientific Reports 2016  Picosecond dynamics of a shockdriven displacive phase transformation in Zr
 TD Swinburne†, MG Glavicic, KM Rahman, NG Jones, J Coakley, DE Eakins, TG White, V Tong, D Milathianaki, GJ Williams, D Rugg, AP Sutton† and D Dye†
Physical Review B 2016
Preprints / Submitted / In Prep
Accepted
In Press
Software
 PyGT Graph Transformation in Python
 Stable analysis of metastable Markov chains
J Chem. Th. Comp. 2020
 PAFI Projected Average Force Integrator
 Anharmonic free energy barrier evaluations using in LAMMPS/MPI/C++
Physical Review Letters 2018
 TAMMBER Temperature Accelerated Markov Models with Bayesian Estimation of Rates
 Branch of ParSplice code for massively parallel rate matrix contruction
Physical Review Materials 2018
Team
 Clovis Lapointe
PhD Student, machine learning techniques for point defects. Cosupervised with Cosmin Marinica, CEA Saclay.
 Petr Grigorev
Postdoc, uncertaintydriven exploration of defective materials within the ANR MEMOPAS project (PI: TDS).
 Deepti Kannan (External Supervision, University of Cambridge)
MSc Student, Coarse graining techniques for Markov Models (under Prof. David Wales)
 Raynol Dsouza (External Supervision, MPIE Dusseldorf)
PhD Student, mean field bond models for anharmonic lattice vibrations (under Prof. Joerg Neugebauer)
Job Openings

We have no current postdoc / PhD openings.
If you are interested in masters projects at AMU, or are considering an independent funding application, 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  Uncertaintydriven construction of Markov models from accelerated molecular dynamics
 Advances in Computational Statistical Physics, CIRM, France, September 2018 (Invited)
Slides  Fast, vacancyfree 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