- 🔭 I’m currently working on machine-learning models to decode X-ray absorption spectroscopy, aiming to understand the morphological and structural information of nanomaterials.
- 🌱 To achieve this goal, I’m currently deepening my knowledge in the fundamental physics behind XAS, neural network architectures, global optimization techniques (such as genetic algorithm and simulated annealing), advanced Python programming, computer algorithms, and chemical visualization tools like Blender, Vista, and Matplotlib.
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University of Stony Brook
- Stony Brook
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12:42
(UTC -04:00) - www.linkedin.com/in/kaifeng-zheng
- https://orcid.org/0000-0001-6766-1805
Highlights
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Mathematics-Optimization
Mathematics-Optimization PublicThis repository is for optimization problems using multiply programming languages, mainly MATLAB for now.
MATLAB
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xraylarch
xraylarch PublicForked from xraypy/xraylarch
Larch: Applications and Python Library for Data Analysis of X-ray Absorption Spectroscopy (XAS, XANES, XAFS, EXAFS), X-ray Fluorescence (XRF) Spectroscopy and Imaging, and more.
Python
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pymatgen
pymatgen PublicForked from materialsproject/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 …
Python
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CNN_EXAFS_PDF
CNN_EXAFS_PDF PublicThis repo contains all codes related to the work entitled "Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-ray Absorption Spectroscopy Data by Machine Learning"
Jupyter Notebook
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