Deep neural networks for density functional theory Hamiltonian.
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Updated
Oct 7, 2024 - Python
Deep neural networks for density functional theory Hamiltonian.
Notes and tutorials on Density Functional Theory calculation using Quantum Espresso.
A code for generating irreducible site-occupancy configurations
This repository contains example codes for the book: Quantum ESPRESSO Course for Solid‑State Physics, Jenny Stanford Publishing, New York, 372 Pages (2022) by N. T. Hung, A. R. T. Nugraha and R. Saito.
Extended DeepH (xDeepH) method for magnetic materials.
The quasiparticle self-consistent GW method in the PMT method (LAPW+LMTO+Lo).
Open-source first-principles computational toolkit for the efficient calculation of the strength of materials in 1D, 2D, and 3D materials at both zero and finite temperatures
A open-source program for computing the first-order resonance Raman spectroscopy based on Quantum ESPRESSO
Ab initio calculation of the spin Hall conductivity in topological insulators using Quantum ESPRESSO and Wannier90 codes.
A compressive-shear biaxial stress field model for analyzing the anisotropic mechanical behavior of crystal (Developed based on Python).
Automate Quantum Espresso routines
This repository contains several ground state Density Functional Theory (DFT) calculations within the framework of Quantum Espresso of various bi-layers of 2D Transition Metal Dichalcogenides as their potential use as nano energy generators along with their electronic structures and structural stability calculations.
A Julia-Python interface of https://github.com/MineralsCloud/qha
PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches
Ab initio calculation of the fully-relativistic electronic band structure of the group 14 monolayers using Quantum ESPRESSO
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