Hi I am Paolo 
,
As a materials modeling researcher at the Politecnico di Torino, I specialize in applying computational methods to energy materials.
My PhD, which I earned cum laude in 2022, focused on reactive and non-reactive interface modeling for energy materials.
My educational background is in mechanical engineering, but I have cultivated expertise in mathematics and materials physics throughout my studies.
Programming is a critical component of my work, and I have developed proficiency in several coding languages. Python is my preferred language due to its extensive community and the ease with which it can be used to wrap high-performance codes for use on High-Performance Computing (HPC) systems. I am experienced in modeling at various space-time scales, but I am particularly drawn to discrete models, such as Molecular Dynamics (MD) and Density Functional Theory (DFT) models. In recent years, I have leveraged my experience in GPU computing to build and train Neural Networks (NN) models, with a focus on NN Force Fields for MD simulations and Machine Learning (ML) tools for material discovery.
My passion for exploring new computational tools and modeling techniques has enabled me to stay at the forefront of my field, and I am trying to push, together with all the other colleagues, the boundaries of human knowledge 🚀.
Python 3
90%
MatLab
85%
Bash/Bourne shell
80%
LaTeX
80%
C
65%
C++
60%
Fortran
60%
HTML5/CSS3
50%
Adobe Illustrator
90%
Adobe Photoshop
85%
Adobe InDesign
70%
Adobe Premiere
70%
Arduino
85%
Microsoft Word
98%
Microsoft PowerPoint
95%
Microsoft Excel
85%
Molecular Dynamics (MD)
90%
Density Functional Theory (DFT)
75%
Computational fluid dynamics (CFD)
70%
Monte Carlo (MC)
65%
Finite Element Method (FEM)
60%
Windows OS
95%
Linux OS
85%
Mac OS
70%
As a Postdoctoral researcher, I am currently focused on advancing my research by further developing models for Lithium-Ion Batteries degradation phenomena and creating Reactive Potential s and Neural Network Force Fields for atomistic simulations
During my PhD, I honed my engineering, mathematical, and physical skills in materials science for energy applications, specifically Nanofluids and Lithium-Ion Batteries. I earned a PhD degree with a grade of 110L after successfully defending my thesis titled "Reactive and Non-Reactive Interface Modelling for Energy Materials."
During my visit at the UIC (which was partially affected by the COVID-19 pandemic), I applied Convolutional Neural Network models to study and predict battery thermal runaway.
I completed the course of study held in English and graduated with a grade of 110, along with writing a thesis titled "Simulation of the ensemble and cycle-resolved combustion processesin a NG-H2 engine."
As a student intern, I worked on a project related to the GasOn initiative, a European effort led by Renault and involving several automobile manufacturers. My role focused on studying numerical stability and combustion model efficiency
I completed my Mechanical Engineering course held in Italian, and I earned a BSc degree with a grade of 107 with a thesis titled "Quaternion application to 3D roto-translation on simulative environment."
During the summer breaks, I worked as an apprentice in a job shop, I helped produce intermediate materials and drew components for external suppliers, gaining experience with manual drilling machines, MIG welders, and bandsaws. I also learned to carry out basic tasks like bearing mounting, assembly, and electrical motor maintenance.