| diego.ugarte@phys.s.u-tokyo.ac.jp | |
| TEL | |
| Room | Faculty of Science Bldg.1, Room 410 |
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Research Field
Machine Learning, Computational Physics, Molecular Dynamics, High-Performance Computing, GPU Programming, Biophysics, Statistical Physics
Research Subject
Machine learning methods for modeling complex biological systems, development of coarse-grained force fields for biological membrane simulations, and optimization of molecular dynamics software on high-performance computing platforms.
Current Research
My research focuses on the development of computational methods that combine machine learning and molecular simulations to study complex biological systems. In particular, I work on graph neural network–based models to recover atomic-resolution structures from coarse-grained representations. I am also involved in accelerating the GENESIS molecular dynamics software on GPU architectures and integrating it with emerging machine-learning-based force fields. In addition, I develop coarse-grained models such as the implicit solvent lipid force field iSoLF for large-scale simulations of biological membranes.
Keywords
Machine Learning, Molecular Dynamics, High-Performance Computing, GPU Computing, Biomolecular Simulations, Statistical Physics