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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