Mihai-Cosmin Marinica

Université Paris-Saclay

France

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Articles (11)

Year:

2023

Year:

2023

Year:

2023

Year:

2023

We explore the performance and applicability range of AutoEncoder neural networks, coupled with Adaptive Biasing Force, in computing free energy barriers at finite temperature for defect processes in materials.

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Collaborators (11)

Nigel Jennett

Coventry University

UNITED KINGDOM

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Carlo Guerrero

Profesor Ayudante Doctor

Universidad de Alcalá Facultad de Ciencias

SPAIN

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jean-bernard maillet

Université Paris-Saclay

FRANCE

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Tymofii Khvan

Assistant Professor

National Centre for Nuclear Research

POLAND

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charlotte becquart

Professeur des Universités

FRANCE

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Maria J. Caturla

Full professor

Universitat d'Alacant

SPAIN

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Juan Pablo Balbuena

Lecturer in Applied Mathematics

Universidad de Alcalá

SPAIN

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Emmanuel Clouet

Université Paris-Saclay

FRANCE

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Cristelle Pareige

Head of the Metallurgy, Microstructure, Mechanics department - Full Professor

University of Rouen

FRANCE

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F. Soisson

Université Paris-Saclay

FRANCE

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Kan MA

University of Birmingham

UNITED KINGDOM

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Mihai-Cosmin Marinica

Research Interests

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Contact this professor

Articles (11)

Compact A15 Frank-Kasper nano-phases at the origin of dislocation loops in face-centred cubic metals

Calculation of dislocation binding to helium-vacancy defects in tungsten using hybrid ab initio-machine learning methods

Anharmonic thermo-elasticity of tungsten from accelerated Bayesian adaptive biasing force calculations with data-driven force fields

Numerical investigation of dislocation climb under stress and irradiation

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects

On Sampling Minimum Energy Path

Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science

Multiscale modelling for fusion and fission materials: the M4F project

Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W

Perspectives on multiscale modelling and experiments to accelerate materials development for fusion

Collaborators (11)

Nigel Jennett

Carlo Guerrero

jean-bernard maillet

Tymofii Khvan

charlotte becquart

Maria J. Caturla

Juan Pablo Balbuena

Emmanuel Clouet

Cristelle Pareige

F. Soisson

Kan MA

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