Christian Venturella

Research

I’m interested in quantum chemistry, materials science, and artificial intelligence, with a focus on developing efficient and accurate methods for predicting many-electron properties beyond standard DFT. My work aims to bring together modern machine learning approaches with traditional physical chemistry to address challenges in spectroscopy, materials science, and catalysis.

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

Christian Venturella, Jiachen Li, Christopher Hillenbrand, Ximena Leyva Peralta, Jessica Liu, Tianyu Zhu*

Nature Computational Science, 2025

Paper ArXiv Code Dataset

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

Christian Venturella, Christopher Hillenbrand, Jiachen Li, and Tianyu Zhu*

J. Chem. Theory Comput. 2024, 20, 1, 143–154, 2024

Paper ArXiv

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