About Me
I'm a Ph.D. student in Computer Science at the University of Liverpool (UK), under the supervision of Yannis Goulermas, Vladimir Gusev, Michael Gaultois, Rahul Savani, Matthew Rosseinsky, and in collaboration with the Leverhulme research centre for functional materials design. I received my MSc degree in Pure and Applied Mathematics from the University of Rome "Tor Vergata" (IT).
What I do
I work in the exciting field of AI4Science, specializing in AI-driven materials discovery. My research encompasses deep learning for material property prediction, representation learning, and inverse materials design using deep generative models. Currently, I am particularly interested in Geometric Graph Neural Networks, Contrastive Learning, and Diffusion Models.
You can find a list of my publications here.
Material property prediction
I develop and utilize ML frameworks to predict chemical properties of atomistic systems, accelerating the discovery of new compounds for applications.
Generative design
I am currently exploring generative models for inverse materials design, with a specific focus on diffusion models for crystal structure prediction (CSP).
Industry Projects
Discovery of new Transparent Conductors using Machine Learning
Oct 2020 — ongoingWe propose a Machine Learning guided search to accelerate the discovery of new transparent conducting materials, an important class of semiconductors with a wide range of applications.
Funded by NSG Group