Poster De Conférence Année : 2025

Screening new Entropy stabilized oxides by DFT calculations and active learning

Résumé

The Entropy Stabilized Oxides (ESO) are the MOx oxides where the metallic site M is a mixture of at least 5 elements with a composition between 5 and 35%. At high temperatures, these materials are stabilized by the configuration entropy. The ESO can have some special properties, such as a high dielectric constant, and can be used as catalysts, supra-conductors and electrolytes in Li-batteries [1]. Considering a base of 16 cationic elements, this leads to 4368 of possible quinary equimolar ESO for each new structure. With the addition of a 1% step variation in composition, it’s up to 2.4 billion possibilities. Our work aims to predict, through DFT calculations and machine learning, which ESO could be a meta-stable candidate and what would be the temperature of stabilization. We use the Special Quasirandom Structure (SQS) to model the multi-component mixing at the cation sites [2]. Then, some DFT calculations are used to calculate the formation enthalpy of oxides to build a learning database. Thanks to a convex hull home-made code, we can determine which phases are the most stable one. The entropy, considered only as configuration entropy, is approximated from the Boltzmann’s formula, with the equation: S = P xi ln(xi). With the 4368 of possible equimolar quinary ESO in the NaCl structure, we built a learning database from high throughput DFT calculations, comprising approximately 10% of the entire combination set. Using supervised machine learning, we aimed to predict the formation enthalpy of all configurations to an accuracy of a few kJ/mol, with a selection of potential candidates was proposed for experimental verification. Despite the use of this model, DFT calculations can remain time-consuming, and many compositions may not be feasible to synthesize. Therefore, we propose the use of Bayesian optimization (BO) [3-4], a machine learning model that quickly identifies the optimum inputs for optimizing a chosen criterion. Thus, the BO can be applied to design ESO based on a new crystal family to establish the set of elements with the lowest stabilization temperature. [1] N. Dragoe and D. B´erardan Science, 366, 573 (2019) [2] A. Zunger et al. Physical Review Letters, 65, 353 (1990) [3] J. Mockus Journal of Global Optimization, 4, 347 (1994) [4] C. Bajan and G. Lambard Chemometrics and Intelligent Laboratory Systems, 258, 105323 (2025)

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Dates et versions

hal-05582906 , version 1 (07-04-2026)

Identifiants

  • HAL Id : hal-05582906 , version 1

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Sébastien Junier, Céline Barreteau, Christophe Bajan, Guillaume Lambard, Jean-Claude Crivello. Screening new Entropy stabilized oxides by DFT calculations and active learning. Toyota Workshop, Mar 2025, Nagoya, Japan. ⟨hal-05582906⟩
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