Deep neural networks for density functional theory Hamiltonian.
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Updated
May 22, 2024 - Python
Deep neural networks for density functional theory Hamiltonian.
Electronic structure Python package for post analysis and large scale tight-binding DFT/NEGF calculations
nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset
Open-source library for analyzing the results produced by ABINIT
Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)
Python package to analyse electron density & electrostatic potential grids
Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.
GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
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Document and code of python and PySCF approach XYG3 type of density functional 2nd-derivative realization
In silico chemical library engine for high-accuracy chemical property prediction
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Exchange correlation functionals translated from libxc to jax
Python tool to manipulate Gaussian cube files
A toolbox for quickly build inputs and analyze results of DFT codes
A physics computational framework for python and ipython
Python tools for automating routine tasks encountered when running quantum chemistry computations.
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
Extended DeepH (xDeepH) method for magnetic materials.
A software for automating materials science computations
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