AI4Science
Artificial Intelligence for Scientific Discovery

How can we detect, classify, and predict relevant patterns in scientific data if they are hidden within large amount of non-relevant data?


Research highlights

Classical DFT

We introduce a method for learning a neural-network approximation of the Helmholtz free-energy functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials.

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Nanoscale Imaging and Metrology

We explore deep learning and Bayesian optimization techniques to push the boundaries of compact semiconductor metrology tools. Label-free optical imaging methods with a spatial resolution beyond the Abbe diffraction limit and a temporal resolution beyond the Nyquist limit are being developed to characterize multi-layer nanostructures.

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Protein Stabilization for Vaccine Design

The stability of the antigen is crucial for the development of effective vaccines against highly contagious viruses. By training deep-learning models to suggest mutations and predict protein stability, we aim to accelerate vaccine research and design.

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News and Events

23 May 2025

AI4Physics lecture series

Journal club style tutorials on deep learning architecturs for scientific applications in physics
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