BowenD-UCB
PhD student at UC Berkeley | Materials Science | Deep Learning
University of California, Berkeley
Pinned Repositories
chgnet_fork
Pretrained universal neural network potential for charge-informed atomistic modeling
matgl
Graph deep learning library for materials
MDsampler
GNN-based sampler for selecting representative structures in MD trajectories
pymatgen-private
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
pymatgen
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
matgl
Graph deep learning library for materials
jarvis_leaderboard
Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w
BowenD-UCB's Repositories
BowenD-UCB/matgl
Graph deep learning library for materials
BowenD-UCB/MDsampler
GNN-based sampler for selecting representative structures in MD trajectories
BowenD-UCB/chgnet_fork
Pretrained universal neural network potential for charge-informed atomistic modeling
BowenD-UCB/pymatgen-private
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.