arXiv subjects

Fabien Pascale

Publications and source records attributed to Fabien Pascale.

4 recordsLinked to original sources

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

Synergistic Effects of Phosphorus Doping and Oxygen Vacancies on Formaldehyde Oxidation over CeO$_2$(111): A First Principles Investigation

Using a combination of static and dynamic density functional theory simulations, we systematically investigated how phosphorus doping and oxygen vacancies on the CeO$_2$(111) surface influence the oxidation mechanisms of formaldehyde (HCHO). Our results reveal that P cations (P$^{5+}$) substitutionally replace Ce$^{4+}$ in the lattice, forming Ce$-$O$-$P bonds that reduce the band gap (from 2.26 eV to 2.09 eV) and generate localized Ce$^{3+}$ states through charge redistribution. This synergistic effect of P doping combined with oxygen vacancy strengthens HCHO adsorption by decreasing the adsorption energy from -0.62 eV on pristine CeO$_2$(111) to -2.65 eV on the defective P-doped surface. Importantly, P doping lowers the C$-$H bond cleavage barrier by 0.84 eV relative to pristine CeO$_2$(111), accelerating formaldehyde oxidation on the defective surface. In addition, the rapid desorption of CO$_2$ and H$_2$O ($\tau \sim 0.59 s$ at 300 K) indicates weak product-surface interactions, which favor efficient catalyst regeneration during continuous operation. These findings highlight P-doped CeO$_2$(111) as a promising system for low-temperature HCHO oxidation and provide insights into the design of ceria-based catalytic materials.

cond-mat.mtrl-sci↗

arXiv2021

Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond

Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of ab initio theory and/or to apply highly expensive computational methods. Indeed, starting from a production molecular dynamics trajectory, this method can estimate properties at one or more target levels of theory from only a small number of additional fixed-geometry calculations, which are used to train a machine learning model. However, as MLPT is based on thermodynamic perturbation theory (TPT), inaccuracies might arise when the starting point trajectory samples a configurational space which has a small overlap with that of the target approximations of interest. By considering case studies of molecules adsorbed in zeolites and several different density functional theory approximations, in this work we assess the accuracy of MLPT for ensemble total energies and enthalpies of adsorption. The problematic cases that were found are analyzed and it is shown that, even without knowing exact reference results, pathological cases for MLPT can be detected by considering a coefficient that measures the statistical imbalance induced by the TPT reweighting. For the most pathological examples we recover target level results within chemical accuracy by applying a machine learning-based Monte Carlo (MLMC) resampling. Finally, based on the ideas developed in this work, we assess and confirm the accuracy of recently published MLPT-based enthalpies of adsorption at the random phase approximation level, whose high computational cost would completely hinder a direct molecular dynamics simulation.

cond-mat.mtrl-sci↗

arXiv2020

Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids

Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical and physical properties of a broad range of systems. With the aim of improving the accuracy of predictions, a large number of representations for molecules and solids for machine learning applications has been developed. In this work we propose a novel descriptor based on the notion of molecular graph. While graphs are largely employed in classification problems in cheminformatics or bioinformatics, they are not often used in regression problem, especially of energy-related properties. Our method is based on a local decomposition of atomic environments and on the hybridization of two kernel functions: a graph kernel contribution that describes the chemical pattern and a Coulomb label contribution that 1encodes finer details of the local geometry. The accuracy of this new kernel method in energy predictions of molecular and condensed phase systems is demonstrated by considering the popular QM7 and BA10 datasets. These examples show that the hybrid localized graph kernel outperforms traditional approaches such as, for example, the smooth overlap of atomic positions (SOAP) and the Coulomb matrices.

physics.chem-ph↗