Caltech · Hygge Lab
Bridging Scales from Atoms to Materials
We develop multiscale simulation methods and machine learning frameworks to understand and design advanced materials and biomolecular systems.
Latest News
Recent updates from the lab
New paper accepted in Journal of Chemical Theory and Computation on machine learning force fields for biomolecular systems.
Congratulations to Minjae Kim for winning the Best Poster Award at the 2025 Korean Physical Society Spring Meeting.
Dr. Jang will give an invited talk at the International Symposium on Multiscale Modeling in Seoul.
Welcome Jisoo Park, who joins Hygge Lab as a new PhD student. We are glad to have you!
Research Areas
We combine computational physics, chemistry, and machine learning
Developing methods that seamlessly bridge quantum mechanics, molecular dynamics, and continuum models to capture phenomena across length and time scales.
Leveraging deep learning and physics-informed neural networks to accelerate the discovery and optimization of novel functional materials.
Investigating the structure, dynamics, and function of proteins, nucleic acids, and their interactions using advanced sampling methods.
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