Yingze Wang
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYingze Wang is a PhD candidate at UC Berkeley with five years of experience bridging theoretical and computational chemistry and applied machine learning. They focus on force field development and ML-driven interatomic models, bringing research-grade rigor to practical engineering problems. As an active contributor to the deepmodeling/dpgen project, Yingze integrated Gromacs with deep learning potentials (including deepmd-kit 2.0), added restart-from-checkpoint support, and resolved subtle issues in model-devi activation and charge-model handling. Based in Berkeley, they are adept at turning ML chemistry prototypes into robust, reproducible workflows.
5 years of coding experience
University of California, Berkeley
Github Skills (9)
model-building10
machine-learning10
python10
modeling10
model-driven10
gr10
model-driven-development10
grok10
active-learning9
Programming languages (4)
Github contributions (5)
deepmodeling/dpgen
Jul 2021 - Sep 2021
The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field
Role in this project:
ML Engineer
ML EngineerContributions:6 reviews, 17 commits, 3 PRs in 1 month
Contributions summary:Yingze primarily contributed to enhancing the Gromacs engine within the deep potential generator, `dpgen`. Their work involved integrating Gromacs with deep learning models, including adding support for deepmd-kit 2.0 and different-charge model_devi systems. They also added support for various features, such as restarting from checkpoints, and fixed bugs related to model-devi activation functions and trajectory frequency.
model-baseddeep-learningfieldforce-fieldforce
Ericwang6/dpdata
Apr 2021 - Nov 2021
Manipulating DeePMD-kit, VASP, LAMMPS data formats.
Contributions:14 PRs, 73 pushes in 6 months
linked-open-datalammpspythoncreditworkitem
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