422 citations · 981 across the 25 of their papers we have counts for
26 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…
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…
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…
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…
Searching for fixed points of dynamical systems by the Explorative Relaxation Redistribution Method
Eliodoro Chiavazzo
Being able to effectively locate saddle (and other fixed) points in dynamical systems holds tremendous implications in a number of applications in engineering and science, among wh…