1 citations · 2 across the 3 of their papers we have counts for
3 papers
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…
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…