arXiv subjects

Rodrigo Ferreira

Publications and source records attributed to Rodrigo Ferreira.

3 recordsLinked to original sources

arXiv2026

Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy

Graph neural networks are promising architectures for fast, accurate and transferable predictions of core-electron binding energies, which depend on the local bond environment. Here we present a graph neural network model for predicting carbon 1s core-electron binding energies in organic molecules. The model is trained with multiconfiguration pair-density functional theory on 8637 carbon atoms in 2116 molecules with 4-16 atoms and evaluated against 570 experimental values in 113 different molecules containing 3-45 atoms. Previous work benchmarked a mean absolute error of 0.27 eV to experiment for the training data level of theory [J. Phys. Chem. A 2025, 129, 36, 8419-8431] and the present model demonstrates an experimental evaluation error of 0.33 eV with good size transferability to larger organic molecules. An equivariant graph neural network is benchmarked against its rotationally invariant analogue and a model comprised of the smooth overlap of atomic positions descriptors and kernel ridge regression for training data efficiency and stability to non-equilibrium geometries absent from the training data. All models show good training data efficiency and the graph based models have improved transferability to non-equilibrium geometries. The use of chemically informed, graph-normalized node features reduces the graph neural network's dependence on message passing depth. A case study on the 45 atom avobenzone tautomers demonstrates the model's ability for instant and precise analysis of complex molecules. The software and data are provided by the open-source AugerNet package at https://doi.org/10.5281/zenodo.19689244.

physics.chem-ph↗

arXiv2026

Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy

Data privacy and eXplainable Artificial Intelligence (XAI) are two important aspects for modern Machine Learning systems. To enhance data privacy, recent machine learning models have been designed as a Federated Learning (FL) system. On top of that, additional privacy layers can be added, via Differential Privacy (DP). On the other hand, to improve explainability, ML must consider more interpretable approaches with reduced number of features and less complex internal architecture. In this context, this paper aims to achieve a machine learning (ML) model that combines enhanced data privacy with explainability. So, we propose a FL solution, called Federated EXplainable Trees with Differential Privacy (FEXT-DP), that: (i) is based on Decision Trees, since they are lightweight and have superior explainability than neural networks-based FL systems; (ii) provides additional layer of data privacy protection applying Differential Privacy (DP) to the Tree-Based model. However, there is a side effect adding DP: it harms the explainability of the system. So, this paper also presents the impact of DP protection on the explainability of the ML model, analyzing the obtained results for SHAP (SHapley Additive exPlanations) and Mean Decrease in Impurity (MDI).

cs.LG↗

arXiv2023

Response to "On the giant deformation and ferroelectricity of guanidinium nitrate" by Marek Szafrański and Andrzej Katrusiak

Following a well-established practice of publishing commentaries to articles of other authors who work on materials that were earlier studied by them (n.b. six published comments[1-6]), Marek Szafrański(MS) and Andrzej Katrusiak (AK) have filed on the preprint server arXiv a manuscript entitled "On the giant deformation and ferroelectricity of guanidinium nitrate"[7] with comments on our article "Exceptionally high work density of a ferroelectric dynamic organic crystal around room temperature" published in Nature Communications (2022, 13, 2823).[8] Both in the submitted comment as well as in the required (by the journal) direct communication with us preceding its posting, MS and AK have expressed dissatisfaction with the choice of literature references in our article, for which they felt that their previous work on this material has not been cited to a sufficient extent. In their comment, they summarize their other remarks on our article as "the structural determinations of GN [guanidinium nitrate] crystals, their phase transitions and associated giant deformation, as well as its detailed structural mechanism, the molecular dynamics and dielectric properties were reported before, while the semiconductivity, ferroelectricity, and fatigue resistance of the GN [guanidinium nitrate] crystals cannot be confirmed."[7] Apart from the sentiments of MS and AK on our choice of cited literature, we find their comments on the scientific content of our article to be strongly biased towards their own results and unfounded. Below, we provide a detailed response to their comments.

cond-mat.mtrl-sci↗