arXiv subjects

Olivier Masson

Publications and source records attributed to Olivier Masson.

7 recordsLinked to original sources

arXiv2026

Ordered-to-disordered transfer learning with graph neural networks for formation-energy and HOMO-LUMO gap prediction in high-entropy perovskite oxides

High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction. Graph neural networks (GNNs) enable rapid exploration of materials space but are often limited by the availability of representative training data. Here, we investigate ordered-to-disordered transfer learning using GNNs for formation-energy and HOMO-LUMO gap prediction in HEPOs by transferring knowledge learned from chemically ordered perovskites. Four representative GNN models, including CGCNN, GATGNN, ALIGNN and M3GNet are evaluated to understand the role of structural representations, spanning pairwise two-body and angular three-body interactions in transfer performance. We find strong property-dependent transfer behavior: formation-energy prediction transfers effectively to disordered HEPOs, whereas HOMO-LUMO gap prediction shows limited transferability due to its sensitivity to local chemical environments. Incorporating a small HEPO-specific training dataset substantially improves HOMO-LUMO gap prediction. Representation-level analysis using UMAP further highlights the importance of encoding three-body geometric information such as in ALIGNN for capturing complex structure-property relationships and improving transferability.

cond-mat.mtrl-sci↗

arXiv2026

Mapping the influence of symmetry breaking in structure-property relationships of ABO$_3$ perovskites

Perovskite oxides have emerged as an important class of material with promising energy applications owing to their compositional and structural flexibility, which enables stabilization of both low- and high-symmetry phases and gives rise to diverse physical properties. Under ambient conditions, most perovskites adopt low-symmetry structures characterized by octahedral tilting and B-site displacements. Despite their importance, computational studies have largely focused on the ideal cubic phase as modeling these distortions remains challenging. The difficulty stems from the absence of a quantitative framework capable of capturing composition-dependent distortions that can occur through multiple non-equivalent atomic displacement modes, often requiring computationally expensive large supercells to explore the structural landscape. Consequently, the influence of distortions on the stability and properties of low-symmetry perovskites remains insufficiently understood. In this work, we develop an efficient computational framework for the rapid construction and exploration of composition-dependent structural models across both low- and high-symmetry phases. Using $\textit{symmetry constrained templates}$ and $\textit{unconstrained supercell templates}$, we systematically investigate 15 representative compositions to uncover relationships between composition, supercell size and shape, and distortion patterns. Based on these insights, we propose a robust and computationally inexpensive protocol for rapid structural exploration and assess the influence of different distortion modes on key physical properties.

cond-mat.mtrl-sci↗

arXiv2026

Modeling phase separation in polymer-derived silicon carbonitride ceramics through extended machine learning molecular dynamics

Polymer-derived ceramics combine the thermal stability of ceramics with the versatile properties of carbon domains, but modeling their atomic-scale evolution during processing remains elusive due to the limitations of traditional computational methods. To address this issue, here we develop and apply a machine learning interatomic potential for silicon carbonitride-based (Si-C-N-H) systems, trained on a diversified database of over 9000 configurations -- including amorphous models, high-temperature states, surfaces, and crystal structure predictions -- to capture the full complexity of these materials. This potential enables large-scale molecular dynamics simulations of 8000-atom systems revealing the atomic-scale evolution of the polymer-derived ceramic during thermal treatment. A key result of this work is the occurrence of a phase separation where carbon domains progressively nucleate from the amorphous SiCN matrix during thermal processing, forming distinct graphene-like sheets while preserving the integrity of the ceramic network. The resulting models reproduce the experimental atomic pair distribution functions with exceptional fidelity, validating our approach and providing microscopic explanations for the material unique combination of ceramic and graphitic properties. In this process, defective 5- and/or 7-member carbon rings, mediate the transformation to stable 6-member aromatic structures. These findings offer new atomic-scale insights into the thermal stability and structural transformation pathways of polymer-derived ceramics, while our methodology opens avenues for studying complex amorphous systems with first-principles accuracy at experimentally relevant scales.

cond-mat.mtrl-sci↗

arXiv2025

Ab-initio study of structural, vibrational and non-linear optical properties of (TiO2)-(Tl2O)-(TeO2) glasses

This paper reports on a systematic first-principles molecular dynamics investigation of binary (TlO$_{0.5}$)$_{y}$-(TeO$_2$)$_{1-y}$ and ternary $(TiO$_{2}$)$_{x}$-(TlO$_{0.5}$)$_{y}$-(TeO$_2$)_{1-x-y}$ tellurite glasses. The obtained structural models are validated against available measured X-ray pair distribution functions. In the binary system, increasing TlO$_{0.5}$ content induces network depolymerization through the reduction of Te coordination number, the substitution of Te-O-Te linkages with Te=O$^{-}$...Tl$^{+}$ units, and the proliferation of non-bridging oxygens. In addition, rings analysis demonstrates a loss of the network connectivity via the opening of small n-membered rings. In contrast, TiO$_2$ acts as a network former in ternary glasses, preserving Te coordination number, and promoting a high fraction of bridging oxygens. Ti atoms induces a network repolymerization that manifests through the formation of smaller Ti-containing n-membered rings thereby balancing the strong effect of Tl$_2$O modifier. Beside the structural analysis, we also computed Raman spectra and non-linear optical properties on the obtained large periodic models. Our results reproduce experimental trends in Raman band shifts with composition, while nonlinear optical calculations show that <$χ^{(3)}$> remains stable with TlO$_{0.5}$ addition in binary glasses, consistent with experiment. In the case of ternary systems, we find that the inclusion of a small fraction of TiO$_2$ preserves the high optical nonlinearity of the TeO$_2$ network while maintaining the overall network connectivity. These results establish a predictive framework for tailoring the atomic structure and nonlinear optical response of tellurite glasses through the controlled interplay of modifiers nature and concentration.

cond-mat.mtrl-sci↗

arXiv2025

Atomic scale structure and dynamical properties of (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ glasses through first-principles modeling and XRD measurements

We resort to first-principles molecular dynamics, in synergy with experiments, to study structural evolution and Na$^+$ cation diffusion inside (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ (x = 0.10-0.40) glasses. Experimental and modeling results show a fair quantitative agreement in terms of total X-ray structure factors and pair distribution functions, thereby setting the ground for a comprehensive analysis of the glassy matrix evolution. We find that the structure of (TeO$_2$)$_{1-x}$-(Na$_2$O)$_{x}$ glasses deviates drastically from that of pure TeO$_2$ glass. Specifically, increasing the Na$_2$O concentration leads to a reduction of the coordination number of Te atoms, reflecting the occurrence of a structural depolymerization upon introduction of the Na$_2$O modifier oxide. The depolymerization phenomenon is ascribed to the transformation of Te-O-Te bridges into terminal Te-O non bridging oxygen atoms (NBO). Consequently, the concentration of NBO increases in these systems as the concentration of the modifier increases, accompanied by a concomitant reduction in the coordination number of Na atoms. The structure factors results show a prominent peak at 1.4 A, that becomes more and more pronounced as the Na2O concentration increases. The occurrence of this first sharp diffraction peak is attributed to the growth of Na-rich channels inside the amorphous network, acting as preferential routes for alkali-ion conduction inside the relatively stable Te-O matrix. These channels enhance the ion mobility.

cond-mat.mtrl-sci↗

arXiv2021

Riedones3D: a celtic coin dataset for registration and fine-grained clustering

Clustering coins with respect to their die is an important component of numismatic research and crucial for understanding the economic history of tribes (especially when literary production does not exist, in celtic culture). It is a very hard task that requires a lot of times and expertise. To cluster thousands of coins, automatic methods are becoming necessary. Nevertheless, public datasets for coin die clustering evaluation are too rare, though they are very important for the development of new methods. Therefore, we propose a new 3D dataset of 2 070 scans of coins. With this dataset, we propose two benchmarks, one for point cloud registration, essential for coin die recognition, and a benchmark of coin die clustering. We show how we automatically cluster coins to help experts, and perform a preliminary evaluation for these two tasks. The code of the baseline and the dataset will be publicly available at https://www.npm3d.fr/coins-riedones3d and https://www.chronocarto.eu/spip.php?article84&lang=fr

cs.CV↗

arXiv2011

Nonlinear optical properties of TeO$_2$ crystalline phases from first principles

We have computed second and third nonlinear optical susceptibilities of two crystalline bulk tellurium oxide polymorphs: $α$-TeO$_{2}$ (the most stable crystalline bulk phase) and $γ$-TeO$_{2}$ (the crystalline phase that ressembles the more to the glass phase. Third order nonlinear susceptibilities of the crystalline phases are two orders of magnitude larger than $α$-SiO$_{2}$ cristoballite, thus extending the experimental observations on glasses to the case of crystalline compounds. While the electronic lone pairs of Te contribute to those large values, a full explanation of the anisotropy of the third order susceptibility tensor requires a detailed analysis of the structure, in particular the presence of helical chains, that seems to be linked to cooperative non-local polarizabilty effects. Our results demonstrate that first-principles simulations are a powerful predictive tool to estimate nonlinear optical susceptibilitites of materials.

cond-mat.mtrl-sci↗