Pioneering the intersection of machine learning and molecular physics. Developing cutting-edge graph neural networks to unlock the secrets of biomolecular interactions with chemical accuracy.
RANGE is a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism to capture long-range interactions in graph-like structures.
We demonstrate that density-corrected SCAN predicts n-body and interaction energies of hydrated ions with an accuracy approaching coupled cluster theory.
DeePMD-based DNN potentials are not able to correctly represent many-body interactions. This explains their limited ability to predict properties for state points that are not explicitly included in the training process.
Ph.D. in Theoretical Chemistry
University of California, San Diego (2018-2023)
M.S. in Physical Chemistry
Sapienza, University of Rome (2016-2018)
B.S. in Chemistry
Sapienza, University of Rome (2013-2016)
Postdoctoral Researcher
Department of Theoretical Biophysics
Freie Universität Berlin (2023-Present)
machine-learning force fields, equivariant message-passing, long-range interactions, density functional theory, molecular dynamics
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