Christian Venturella

I am a PhD candidate in theoretical chemistry at Yale, where I am advised by Tianyu Zhu. I am an NDSEG fellow working with the Office of Naval Research.

Before my PhD studies, I graduated summa cum laude from Princeton University, where I worked with Greg Scholes for my undergraduate thesis.

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Research

I am interested in quantum chemistry, materials science, and artificial intelligence. Most of my research focuses on developing efficient and accurate methods for predicting many-electron material properties beyond the level of DFT. I broadly aim to combine modern techniques (e.g. AI/ML) and traditional physical chemistry to tackle challenges arising in spectroscopy, materials science, and catalysis.

Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions

A deep learning framework that unifies ground and excited state property prediction by targeting the many-body Green’s function (MBGF) and self-energy. I design and implement a graph neural network called MBGF-Net that learns the underlying physics to derive properties with competitive accuracy, unprecedented data efficiency, and impressive transferability and generalizability.

Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra

A machine learning method for predicting many-body Green’s Functions for realistic molecules and materials.

Design and source code from Jon Barron's website