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20212025
most citedMinimal set of crystallographic descriptors for sorption properties in hypothetical Metal Organic Frameworks: Role in sequential learning optimization

36 citations · 70 across the 7 of their papers we have counts for

collaborators

7 papers

physics.comp-ph2025★ 1 cited

Notes on Quantum Computing for Thermal Science

Pietro Asinari, Nada Alghamdi, Paolo De Angelis +6

This document explores the potential of quantum computing in Thermal Science. Conceived as a living document, it will be continuously updated with experimental findings and insight…

cond-mat.supr-con2025★ 10 cited

Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds

Marnik Bercx, Samuel Poncé, Yiming Zhang +8

We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud 3-dimensional structure database of experimentally kn…

cond-mat.mtrl-sci2024★ 5 cited

Energy-GNoME: A Living Database of Selected Materials for Energy Applications

Paolo De Angelis, Giovanni Trezza, Giulio Barletta +2

Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol iden…

physics.data-an2024★ 2 cited

Learning effective good variables from physical data

Giulio Barletta, Giovanni Trezza, Eliodoro Chiavazzo

We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We…

cond-mat.other2023★ 9 cited

Classification-based detection and quantification of cross-domain data bias in materials discovery

Giovanni Trezza, Eliodoro Chiavazzo

It stands to reason that the amount and the quality of data is of key importance for setting up accurate AI-driven models. Among others, a fundamental aspect to consider is the bia…

cond-mat.other2023★ 7 cited

Leveraging Composition-Based Material Descriptors for Machine Learning Optimization

Giovanni Trezza, Eliodoro Chiavazzo

In this study, we evaluate several classifiers and focus on selecting a minimal set of appropriate material features. Our objective is to propose and discuss general strategies for…

cond-mat.other2021★ 36 cited

Minimal set of crystallographic descriptors for sorption properties in hypothetical Metal Organic Frameworks: Role in sequential learning optimization

Giovanni Trezza, Luca Bergamasco, Matteo Fasano +1

Several studies have been recently reported in the literature on sorption properties of MOFs with a number of organic sorbates, such as ethanol and methanol. Surprisingly, still fe…