GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
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Updated
Feb 13, 2024 - Python
GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
Quantum computational chemistry based on TensorCircuit
Implementation of a machine learned density functional
State Interaction Spin-Orbit (SISO) Method for CASSCF and FCI
Source code for Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method
Automatic diagram and code generation of quantum chemistry coupled-cluster equations
A trial to implement doubly-hybrid interface to PySCF
Implementation of local algorithms within pyscf
Green's function methods using auxiliary space
Fast-randomized iteration for coupled cluster.
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