7 papers
Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series
Giulio Barletta, Simon Ternes, Saif Ali +7
Perovskite solar cells achieve remarkable power conversion efficiencies, yet operational stability remains a major barrier to large-scale deployment. Reliable and rapid assessment…
Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
Nada Alghamdi, Paolo de Angelis, Pietro Asinari +1
Machine learning force fields (MLFFs) are transforming materials science and engineering by enabling the study of complex phenomena, such as those critical to battery operation. In…
Pore-level Quantitative Structure-Activity Relationship (QSAR) for Water Permeation Rate in Aquaporins
Juan José Galano-Frutos, Luca Bergamasco, Paolo Vigo +5
Aquaporins (AQPs) and aquaglyceroporins (AQGPs) play a crucial role in regulating water transport and solute selectivity across biological membranes. Besides their biological relev…
Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT
Nada Alghamdi, Paolo de Angelis, Pietro Asinari +1
The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a m…
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…
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…