This is largely archival; active codes are hosted at https://github.com/tomswinburne

Please see website for full list of publications Tom Swinburne https://tomswinburne.github.io

• PAFI : Linear-scaling evaluation of anharmonic free energy barriers in LAMMPS (PRL 2018)
• TAMMBER : Massively parallel exploration of energy landscapes (NPJ CM 2020)
• LML-RETRAIN : Hybrid ab initio-machine learning simulations of dislocations (Acta Mat 2023)

Funding :

2024-2028: ANR PRC DAPREDIS (see website for open positions, hiring PhD 2024 and postdoc 2025)

2023-2024: CNRS EMERGENCE@INP ParaDiff

2019-2022: ANR JCJC MeMoPas https://anr.fr/Project-ANR-19-CE46-0006

Computational resources from IDRIS, EUROFusion and CEA are also recognized.

Lead: Tom Swinburne https://tomswinburne.github.io
Please see website for full list of publications

Team Members:
Petr Grigorev https://pgrigorev.github.io
Ivan Maliyov

Past Members / Involved Students:
Deepti Kannan (now PhD, MIT)
Reza Namakian (now PostDoc, TAMU)
Arnaud Allera (now PostDoc, CEA)

Collaborators:
Danny Perez (Los Alamos)
David Wales (Cambridge)
Cosmin Marinica (CEA Saclay)

LML-RETRAIN: Hybrid ab initio-machine learning simulations of dislocations

LML_retrain is an advanced coupling scheme to embed small DFT simulations in large-scale MD. To enable this embedding, we retrain (make small parameter adjustments to) linear machine learning potentials, giving seamless coupling between DFT and MD, to significantly extend the scope of hybrid simulation methods.

Code Repository: https://github.com/marseille-matmol/LML-retrain

Publication: Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods Acta Materialia, 2023 https://doi.org/10.1016/j.actamat.2023.118734

Please see links to repositories below for more detail