XanaduAI/GradDFT
GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
This project helps computational chemists and materials scientists design and test new exchange-correlation functionals, which are crucial components in Density Functional Theory (DFT) calculations. It takes in molecular structures and desired properties, then outputs optimized functional parameters that can be used for more accurate simulations of chemical systems. This is for researchers working on advanced quantum chemistry methods.
112 stars. No commits in the last 6 months.
Use this if you are a quantum chemist or computational materials scientist looking to develop and refine custom exchange-correlation functionals using machine learning to achieve higher accuracy in DFT calculations.
Not ideal if you are looking for a straightforward, off-the-shelf DFT package for routine molecular simulations without delving into functional development.
Stars
112
Forks
10
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 13, 2024
Commits (30d)
0
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