SPILADY

A Spin-Lattice Dynamics Simulation Program

CCFE/

UKAEA

Open, Apache License, Version 2.0

T2.1

Requires further development, the code itself is freely available from the CCFE and journal websites.

Spin-lattice dynamics simulation

Performs simulations similar to molecular dynamics, but also taking into account magnetic degrees of freedom, can compute magnetic phase transitionfor a system of moving atoms

It is the only code that can do spin-lattice dynamics treating rotational and longitudinal fluctuations of magnetic moments.

Presently treats only a single type of atoms. Recent developments enabled extending the capabilities to multi-element systems. Requires further development in the treatment of magnetic interactions in complex magnetic systems.

Pui-Wai Ma, S. L. Dudarev, C. H. Woo, Computer Physics Communications Volume 207, October 2016, Pages 350-361

ATOMSK

The Swiss-army knife of atomic simulations

CIEMAT (user)

GNU General Public Licence version 3

T4.1.3

Use as black box

Auxiliary to LAMMPS

Atomsk can be used to transform and shape atomic systems: duplicate, rotate, deform, insert dislocations, merge several systems, create bi-crystals and poly-crystals, etc.

Insert dislocations in bi-crystals

Beta version

"Atomsk: A tool for manipulating and converting atomic data files" Pierre Hirel, Comput. Phys. Comm. 197 (2015) 212-219 | doi:10.1016/j.cpc.2015.07.012

FS-VDIS

Finite Strain Viscoplasticity coupled with Damage for Irradiated FM steel

KIT

In house, closed access

T5.3

To be developed for this project

Constitutive model

Describes deformation and damage behavior of EUROFER

ABAQUS UMAT describing

post yield and post necking behavior

Code under development

J. Aktaa, C. Petersen J.Nucl.Mater. 417 (2011) 1123-1126

AMITEX

Continuum mechanics FFT-based solver for the simulation of heterogeneous microstructures (i.e. polycrystals in M4F context)

CEA

Open access (old version)

Upon demand (current version)

T4.2

Used and improved : coupling with Discrete Dislocation Dynamics

Continuum Mechanics

(including crystal plasticity)

Simulate the stress-strain fields within heterogeneous microstructures (i.e. polycrystals in M4F context)

Massively parallel and

much faster than conventional Finite Element codes

The types of boundary conditions are limited (compared to conventional FE codes)

AMITEX web site: http://www.maisondelasimulation.fr/projects/amitex/html/overview.html

mM

microMegas Dislocation Dynamics code

LEM (CNRS/Onera)

GPL

T4.2

The existing Discrete-Continuous Model (DCM) using FEM solver is extended to FFT solver with AMITEX

Dislocation Dynamics simulation code

Simulate crystal plasticity at the discrete scale of dislocations

A very efficient code (parallel) to calculate plasticity in finite and periodic volume with complex boundary conditions

Large strain simulations and dislocation irradiation defects interactions

O. Jamond et al., IJP, 80:19– 37, 5 2016.

B. Devincre et al., in « Mechanics of Nano-objects”, Presses de l’Ecole des Mines de Paris, 2011.

NUMODIS

Numerical Modelling of Dislocations

CEA/CNRS/INRIA

in-house/available on demand

T.4.2

Used and further developed/upgraded for the coupling with AMITEX

Dislocation Dynamics simulation code

Simulate crystal plasticity at the grain scale based on dislocation theory

Well suited to account for the effects of radiation-induced defects on plasticity

Limited numerical efficiency when it comes to deal with large scale simulations

Drouet et al. (2016). Scripta Materialia, 119, 71-75.

ABAQUS

ABAQUS

European Commission, JRC

METU

Commercial software, finite element solver

T5.1

Use and progressive upgrade (user element)

Crystal plasticity

Adds strain gradient effects to the crystal plasticity approach

Adding non-convexity should enable modelling channel patterning within a grain.

T. Yalçinkaya, W.A.M. Brekelmans, and M.G.D. Geers. Non-convex rate dependent strain gradient crystal plasticity and deformation patterning. International Journal of Solids and Structures, 49(18):2625–2636, September 2012, https://doi.org/10.1016/j.ijsolstr.2012.05.029

MCPP

library for Monte Carlo simulations in cristals

CNRS

In-house, closed access

T5.2

adapt & use

metroplis and kinetic MC simulations

simulates segregation to grain boundaries

Handles vacancies & deformation

does not handle self interstitials, limited by extreme local relaxations

Tanguy D. Phys. Rev. B 72 174116 (2005)

MATEO

simulating irradiation effects in MATerials with Object kinetic monte carlo

SCK•CEN

In-house, closed access.

T2.2

Use and progressive upgrade

Object kinetic Monte Carlo

Describes the microstructure evolution of a material under irradiation; including the solute chemical elements

Applied with success to RPV, F/M, and W-Re system

By construction, focused on bcc or fcc materials (therefore not an issue here)

N. Castin et al., Journal of Nuclear Materials 500 (2018) 15.

Other major publications currently in preparation.

MEGA-OKMC

Microstructure Evolution GPU-based Accelerated Object Kinetic Monte Carlo

CIEMAT

In-house, closed access.

Will be open access soon.

T3.2

Use and progressive upgrade

Object kinetic Monte Carlo

Describes the microstructure evolution of a material under irradiation

It is parallelized using GPU, it is faster than other codes of the same class allowing larger volumes / longer times to be simulated compared with other codes

Has not yet been applied to fully realistic cases

F. Jimenez and C. J. Ortiz, J. Comp. Mater. Sci. 113 (2016) 178

MMonCa

Lattice and off-lattice kinetic Monte Carlo

UA (Developed at IMDEA Materials)

Open access

T2.2

T3.2

Use and progressive upgrade

Kinetic Monte Carlo

Microstructure evolution of materials, surface diffusion, thin film growth

Applied to many differente systems from metals to semiconducctors. Extended for alloy segregation under irradiation.

Parallelization is difficult.

I. Martin-Bragado, A. Rivera, G. Valles, J. L. Gomez-Selles, M. J. Caturla, Mmonca: An object

kinetic monte carlo simulator for damage irradiation evolution and defect diffusion, Computer

Physics Communications 184 (2013) 2703–2710. doi:10.1016/j.cpc.2013.07.011.

MARLOWE

The Computer Simulation of Atomic Collisions in Crystalline Solids

CIEMAT (Nuclear Energy Agency, NEA)

Open access

T3.2

Use and progressive upgrade

Binary Collision Approximation

Calculates the defects that form during cascades

Much faster than MD to simulate cascades.

Has been parallelized with MPI at CIEMAT.

Cannot predict accurately last stages of cascades. Does account for long-range potential between atoms during cooling down phase.

M. T. Robinson and I. Torrens, Phys. Rev. 9 (1974) 5008

BCA-MD

Binary Collision Approximation – Molecular Dynamics

CIEMAT

In-house,

closed access

T3.2

Use and progressive upgrade

BCA+MD

Calculates the defects that form during cascades

Combines BCA and MD. It is much faster than MD, allowing simulation of large number of high-energy cascades

Has not yet been applied to materials with 2 elements (FeCr, SiC…)

Christophe J. Ortiz, J. Comp. Mater. Sci. 154 (2018) 325

LAMMPS

Large-scale Atomic/Molecular Massively Parallel Simulator

CIEMAT (Sandia National Laboratory) UPC CNRS

Open access

T3.2

T4.1

T6.1

T6.2

Use as black box

Molecular Dynamics

Describes the evolution of a material using Newton equations and empirical potentials.

Has potentials for solid-state materials (metals, semiconductors) and soft matter (biomolecules, polymers) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale.

Runs on CPU, GPU and Intel Xeon Phi.

Limited to very small volumes and short times (~ns)

S. Plimpton, J. Comp. Phys. 117 (1995) 1

SIESTA

Spanish Initiative for Electronic Simulations with Thousands of Atoms

CIEMAT (Universidad Autonoma de Madrid, UAM)

Open Access

T3.2

Use and progressive upgrade

Ab Initio Molecular Dynamics

Calculates the defects energy and structural configuration, and describes the evolution of a material using Ab Initio Method

SIESTA is both a method and its computer program implementation, to perform efficient electronic structure calculations and ab initio molecular dynamics simulations of molecules and solids. Runs on CPU clusters.

Limited to very small volumes (thousands of atoms) and short times (~ps)

Soler et al. J. of Physics: Condensed Matter. 14 (11) 2745-2779 (2002)

DFTB+

Density Functional based Tight Binding

CNRS

Open Access

T4.1

Use and progressive upgrade

Quantum mechanical simulations

Does electronic structure calculations based on approximate density function theory

It is parallelized, it is faster than full DFT codes allowing larger volumes to be simulated

Has not yet been applied to bulk metals

B. Aradi, B. Hourahine, and Th. Frauenheim. DFTB+, a sparse matrix-based implementation of the DFTB method, J. Phys. Chem. A, 111 5678 (2007).

VASP

The Vienna Ab initio simulation package

CNRS

CEA

KTH

Commercial software

T2.1, T4.1

Use

Density Function Theory calculations

Does electronic structure calculations based on plane wave density function theory

It is parallelized, fastest and most commonly used DFT code

None

Kresse, Georg, and Jürgen Furthmüller. "Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set." Physical review B 54.16 (1996): 11169.

MCE + AKMC

Magnetic Cluster Expansion + Atomistic Kinetic Monte Carlo simulations

CCFE

CEA Saclay

Develop­met and use

Cluster Expansion

Kinetic Monte Carlo

Thermo., point defects and diffusion properties, including the effects of the magnetic transitions

Parameters fitted to DFT calculations

Rigid lattice approximation

Could be time consuming, especially for kinetic applications

Lavrentiev et al. PRB 81, 184202 (2010)
Senninger et al. Acta Mater 73, 97 (2014)

MILADY

Machine Learning Dynamics

CEA SAclay

In house

T2.1

Development and use

Potential fitting

Can use a wide range of paradigms and methods for machine lerning potetnial fitting

A. M. Goryaeva, W. Unn-Toc, M. C. Marinica, MiLaDy - Machine Learning Dynamics (CEA,Saclay, 2015-2018).