CV HAL - Guillaume Deffrennes

Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics

Quentin Bizot , Ryo Tamura , Guillaume Deffrennes

Computational Materials Science, 2026, 266, pp.114568. ⟨10.1016/j.commatsci.2026.114568⟩

Article dans une revue hal-05500025v1

Accelerating Phase Prediction via CALPHAD-Informed Machine Learning and Data Augmentation

Caroline Binde Stoco , Guillaume Deffrennes , Yannick Champion , Francisco Gil Coury

Computational Materials Science, 2026, 272, ⟨10.1016/j.commatsci.2026.114834⟩

Article dans une revue hal-05782178v1

aLLoyM: a large language model for alloy phase diagram prediction

Yuna Oikawa , Guillaume Deffrennes , Rintaro Shimayoshi , Taichi Abe , Ryo Tamura et al.

npj Computational Materials, 2026, 12 (1), pp.97. ⟨10.1038/s41524-026-01966-6⟩

Article dans une revue hal-05542561v1

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment

Fumiyasu Oba , Takayuki Nagai , Ryoji Katsube , Yasuhide Mochizuki , Masatake Tsuji et al.

Science and Technology of Advanced Materials, 2024, ⟨10.1080/14686996.2024.2423600⟩

Article dans une revue hal-04822051v1

AIPHAD, an active learning web application for visual understanding of phase diagrams

Ryo Tamura , Haruhiko Morito , Guillaume Deffrennes , Masanobu Naito , Yoshitaro Nose et al.

Communications Materials, 2024, 5 (1), pp.139. ⟨10.1038/s43246-024-00580-7⟩

Article dans une revue hal-04728443v1

Data-driven study of the enthalpy of mixing in the liquid phase

Guillaume Deffrennes , Bengt Hallstedt , Taichi Abe , Quentin Bizot , Evelyne Fischer et al.

Calphad, 2024, 87, pp.102745. ⟨10.1016/j.calphad.2024.102745⟩

Article dans une revue hal-04735005v2

Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science

Ai Koizumi , Guillaume Deffrennes , Kei Terayama , Ryo Tamura

Scientific Reports, 2024, 14 (27019), ⟨10.1038/s41598-024-76800-4⟩

Article dans une revue hal-04822050v1

A framework to predict binary liquidus by combining machine learning and CALPHAD assessments

Guillaume Deffrennes , Kei Terayama , Taichi Abe , Etsuko Ogamino , Ryo Tamura

Materials & Design, 2023, 232, pp.112111. ⟨10.1016/j.matdes.2023.112111⟩

Article dans une revue hal-04187514v1

Machine-Learning-Based phase diagram construction for high-throughput batch experiments

Ryo Tamura , Guillaume Deffrennes , Kwangsik Han , Taichi Abe , Haruhiko Morito et al.

Science and Technology of Advanced Materials: Methods, 2022, 2 (1), pp.153 - 161. ⟨10.1080/27660400.2022.2076548⟩

Article dans une revue hal-04728516v1

On models to describe the volume in the context of establishing high-pressure Gibbs energy databases

Guillaume Deffrennes , Jean-Marc Joubert , Benoit Oudot

Calphad, 2022, 78, pp.102435. ⟨10.1016/j.calphad.2022.102435⟩

Article dans une revue hal-03747633v1

A self-consistent model to describe the temperature dependence of the bulk modulus, thermal expansion and molar volume compatible with 3rd generation CALPHAD databases

Guillaume Deffrennes , Benoit Oudot

Calphad, 2021, 74, pp.102291. ⟨10.1016/j.calphad.2021.102291⟩

Article dans une revue hal-04145846v1

Thermodynamics and structural properties of CaO: A molecular dynamics simulation study

Cecilia M.S. Alvares , Guillaume Deffrennes , Alexander Pisch , Noel Jakse

The Journal of Chemical Physics, 2020, ⟨10.1063/1.5141841⟩

Article dans une revue hal-03020747v1

Synthesis, crystallographic structure and thermodynamic properties of T2-Al2MgC2

Guillaume Deffrennes , Bruno Gardiola , Erwann Jeanneau , Georges Mikaelian , Benigni P. et al.

Journal of Solid State Chemistry, 2019, 273, pp.150-157. ⟨10.1016/j.jssc.2019.02.039⟩

Article dans une revue hal-02062499v1

Data-driven study of the enthalpy of mixing in liquid alloys

Guillaume Deffrennes , Quentin Bizot , Bengt Hallstedt , Taichi Abe , Evelyne Fischer et al.

FEMS EUROMAT 2025, Sep 2025, Granada (Spain), Spain

Communication dans un congrès hal-05340249v1

A Data-Driven Study of the Thermodynamic Properties of Mixing for Improved Phase Prediction in Complex Concentrated Alloys

Guillaume Deffrennes , Runan Xie , Quentin Bizot , Ryo Tamura

Journées annuelles 2025 du GdR HEA (Alliages à Haute Entropie et de composition complexe), Apr 2025, Lyon, France

Communication dans un congrès hal-05407428v1

Thermodynamic study of Zr-Ni-Al system

Manon Bornand , M. Fivel , Guillaume Deffrennes , Annie Antoni-Zdziobek , Luanna Carvalho de Souza et al.

THERMEC2025, Jun 2025, Tours, France

Communication dans un congrès hal-05381148v1

Genetic Algorithm integrated with Machine Learning for high toughness High-Entropy Alloy design

Caroline Binde Stoco , Francisco Gil Coury , Guillaume Deffrennes , Yannick Champion

XXII B-MRS Meeting, Sep 2024, São Paulo, Brazil

Communication dans un congrès hal-04822059v1

Using machine learning to study liquids

Guillaume Deffrennes

Journée de prospective sur la physicochimie des liquides à Haute Températures, Nov 2024, Paris, France

Communication dans un congrès hal-04830343v1

Atomistic simulations and active learning of the enthalpy of mixing in liquid and solid solutions

Quentin Bizot , Noël Jakse , Jean-Claude Crivello , Ryo Tamura , Guillaume Deffrennes

CALPHAD 2024, May 2024, Mannheim, Germany

Communication dans un congrès hal-04822053v1

Active learning approach and atomistic simulations for the prediction of the enthalpy of mixing in the liquid

Quentin Bizot , Noël Jakse , Ryo Tamura , Guillaume Deffrennes

TOFA 2024: 19th Discussion Meeting on Thermodynamics of Alloys, Sep 2024, Lyon, France

Communication dans un congrès hal-04822052v1

Impact of data quality on machine learning and improvement of datasets on liquid alloys

Guillaume Deffrennes

Data-driven materials R&D activities, A bilateral dialogue: France-Japan, Office for Science and Technology of the Embassy of France in Japan, the International Research Laboratory Laboratory for INnovative Key materials and structures (LINK) and the National Institute for Materials Science (NIMS), Dec 2024, Tokyo, Japan

Communication dans un congrès hal-04830337v1

THERMAL ANALYSIS FOR MODELING ASSESSMENT AND PYROMETER CALIBRATION

Mathias Desseaux , Rodica Chiriac , Francois Toche , Guillaume Deffrennes , Olivier Dezellus et al.

52 eme Journées de Calorimétrie et d’Analyse Thermique (JCAT-52), Jun 2022, Colmar, Alsace, France

Communication dans un congrès hal-04751878v1

PROCESSING OF A ZR-BASED BULK METALLIC GLASS

Manon Bornand , Marc Fivel , Guillaume Deffrennes , Alexis Lenain , Laura Mangourny et al.

19th Discussion Meeting of Thermodynamics of alloy, Sep 2024, Lyon, France

Poster de conférence hal-04920008v1

A data-driven study of the enthalpy of mixing in the liquid phase

Guillaume Deffrennes , Bengt Hallstedt , Taichi Abe , Quentin Bizot , Evelyne Fischer et al.

CALPHAD 2024, May 2024, Mannheim, Germany

Poster de conférence hal-04822056v1

A data-driven study of the enthalpy of mixing in the liquid phase

Guillaume Deffrennes , Bengt Hallstedt , Taichi Abe , Quentin Bizot , Evelyne Fischer et al.

TOFA 2024: 19th Discussion Meeting on Thermodynamics of Alloys, Sep 2024, Lyon, France

Poster de conférence hal-04822054v1

Machine learning methods to help construct phase diagrams

Guillaume Deffrennes , Kei Terayama , Ryo Tamura

TOFA 2024: 19th Discussion Meeting on Thermodynamics of Alloys, Sep 2024, Lyon, France

Poster de conférence hal-04822055v1

Experimental study and thermodynamic modelling of the Al-C-Mg system

Guillaume Deffrennes , Bruno Gardiola , Erwann Jeanneau , Georges Mikaelian , Benigni P. et al.

International Conference on Computer Coupling of Phase Diagrams and Thermochemistry, CALPHAD 2019, Jun 2019, Singapore, Singapore

Poster de conférence hal-04753621v1

Applications du machine learning en thermodynamique des matériaux

Guillaume Deffrennes

2024

Autre publication scientifique hal-04830352v1

Guillaume Deffrennes

Publications

Publications

Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics

Accelerating Phase Prediction via CALPHAD-Informed Machine Learning and Data Augmentation

aLLoyM: a large language model for alloy phase diagram prediction

Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment

AIPHAD, an active learning web application for visual understanding of phase diagrams

Data-driven study of the enthalpy of mixing in the liquid phase

Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science

A framework to predict binary liquidus by combining machine learning and CALPHAD assessments

Machine-Learning-Based phase diagram construction for high-throughput batch experiments

On models to describe the volume in the context of establishing high-pressure Gibbs energy databases

A self-consistent model to describe the temperature dependence of the bulk modulus, thermal expansion and molar volume compatible with 3rd generation CALPHAD databases

Thermodynamics and structural properties of CaO: A molecular dynamics simulation study

Synthesis, crystallographic structure and thermodynamic properties of T2-Al2MgC2

Data-driven study of the enthalpy of mixing in liquid alloys

A Data-Driven Study of the Thermodynamic Properties of Mixing for Improved Phase Prediction in Complex Concentrated Alloys

Thermodynamic study of Zr-Ni-Al system

Genetic Algorithm integrated with Machine Learning for high toughness High-Entropy Alloy design

Using machine learning to study liquids

Atomistic simulations and active learning of the enthalpy of mixing in liquid and solid solutions

Active learning approach and atomistic simulations for the prediction of the enthalpy of mixing in the liquid

Impact of data quality on machine learning and improvement of datasets on liquid alloys

THERMAL ANALYSIS FOR MODELING ASSESSMENT AND PYROMETER CALIBRATION

PROCESSING OF A ZR-BASED BULK METALLIC GLASS

A data-driven study of the enthalpy of mixing in the liquid phase

A data-driven study of the enthalpy of mixing in the liquid phase

Machine learning methods to help construct phase diagrams

Experimental study and thermodynamic modelling of the Al-C-Mg system

Applications du machine learning en thermodynamique des matériaux