8 papers
Toward the Rational Design of Molecular Field-Coupled Nanocomputing Candidates
Federico Ravera, Leonardo Medrano Sandonas, Andrea Vezzoli +4
Molecular Field-Coupled Nanocomputing (MolFCN) is a promising beyond-CMOS paradigm in which information is propagated electrostatically rather than through charge transport, enabli…
Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
Aleksander Szewczyk, Leonardo Medrano Sandonas, David Bodesheim +2
Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large sur…
Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials
Li Chen, Leonardo Medrano Sandonas, Shirong Huang +2
Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challeng…
Vanadium-doped HfO, multiferroic uncompromised
Vincenzo Fiorentini, Paola Alippi, Gianaurelio Cuniberti
Ab initio density-functional calculations show that orthorhombic Pca21 hafnia HfO2 mixed with vanadium at low concentration is a ferroelectric and ferromagnetic insulator. The mult…
Chirality-induced Spin-Orbit Coupling and Spin Selectivity
Massimiliano Di Ventra, Rafael Gutierrez, Gianaurelio Cuniberti
We show that a spinor traveling along a one-dimensional helical path develops a spin-orbit coupling as a result of the curvature of the path. We estimate the magnitude of the assoc…
On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions
Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm +2
The development of resilient and lightweight Aluminum alloys is central to advancing structural materials for energy-efficient engineering applications. To address this challenge,…