A machine learning–based classification approach for phase diagram prediction von Deffrennes, Guillaume, Terayama, Kei, Abe, Taichi, Tamura, Ryo

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Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science von Koizumi, Ai, Deffrennes, Guillaume, Terayama, Kei, Tamura, Ryo

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Thermodynamics and structural properties of CaO: A molecular dynamics simulation study von Alvares, Cecilia M. S., Deffrennes, Guillaume, Pisch, Alexander, Jakse, Noël

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A framework to predict binary liquidus by combining machine learning and CALPHAD assessments von Deffrennes, Guillaume, Terayama, Kei, Abe, Taichi, Ogamino, Etsuko, Tamura, Ryo

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Data-driven study of the enthalpy of mixing in the liquid phase von Deffrennes, Guillaume, Hallstedt, Bengt, Abe, Taichi, Bizot, Quentin, Fischer, Evelyne, Joubert, Jean-Marc, Terayama, Kei, Tamura, Ryo

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Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment von Oba, Fumiyasu, Nagai, Takayuki, Katsube, Ryoji, Mochizuki, Yasuhide, Tsuji, Masatake, Deffrennes, Guillaume, Hanzawa, Kota, Nakano, Akitoshi, Takahashi, Akira, Terayama, Kei, Tamura, Ryo, Hiramatsu, Hidenori, Nose, Yoshitaro, Taniguchi, Hiroki

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AIPHAD, an active learning web application for visual understanding of phase diagrams von Tamura, Ryo, Morito, Haruhiko, Deffrennes, Guillaume, Naito, Masanobu, Nose, Yoshitaro, Abe, Taichi, Terayama, Kei

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Machine-Learning-Based phase diagram construction for high-throughput batch experiments von Tamura, Ryo, Deffrennes, Guillaume, Han, Kwangsik, Abe, Taichi, Morito, Haruhiko, Nakamura, Yasuyuki, Naito, Masanobu, Katsube, Ryoji, Nose, Yoshitaro, Terayama, Kei

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

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Tin (Sn) at high pressure: Review, X-ray diffraction, DFT calculations, and Gibbs energy modeling von Deffrennes, Guillaume, Faure, Philippe, Bottin, François, Joubert, Jean-Marc, Oudot, Benoit

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On models to describe the volume in the context of establishing high-pressure Gibbs energy databases von Deffrennes, Guillaume, Joubert, Jean-Marc, Oudot, Benoit

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Thermodynamic modelling of the Ca–O system including 3rd generation description of CaO and CaO2 von Deffrennes, Guillaume, Jakse, Noël, Alvares, Cecilia M.S., Nuta, Ioana, Pasturel, Alain, Khvan, Alexandra, Pisch, Alexander

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Synthesis, crystallographic structure and thermodynamic properties of T2-Al2MgC2 von Deffrennes, Guillaume, Gardiola, Bruno, Jeanneau, Erwann, Mikaelian, Georges, Benigni, Pierre, Pasturel, Alain, Pisch, Alexander, Andrieux, Jérôme, Dezellus, Olivier

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Datasets from "A framework to predict binary liquidus by combining machine learning and CALPHAD assessments" von Guillaume Deffrennes

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Datasets from "A framework to predict binary liquidus by combining machine learning and CALPHAD assessments" von Guillaume Deffrennes

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Datasets from "A framework to predict binary liquidus by combining machine learning and CALPHAD assessments" von Guillaume Deffrennes

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Étude expérimentale et évaluation thermodynamique du système Al-C-Mg von Deffrennes, Guillaume

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

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

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On models to describe the volume in the context of establishing high-pressure Gibbs energy databases von Deffrennes, Guillaume, Joubert, Jean-Marc, Oudot, Benoit

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