nguyen-group

nguyen-group's Stars

• whitead/dmol-book

  Deep learning for molecules and materials book

  Language:Jupyter Notebook63615159119

• mzjb/DeepH-pack

  Deep neural networks for density functional theory Hamiltonian.

  Language:Python25975852

• phoebe-team/phoebe

  A high-performance framework for solving phonon and electron Boltzmann equations

  Language:C++9597120

• eemlcommunity/PracticalSessions2024

  Language:Jupyter Notebook694017

• quantumVITAS/quantumVITAS

  Quantum Visualization Interacting Toolkit for Ab-initio Simulations

  Language:Java68161210

• FourPhonon/FourPhonon

  An extension module to ShengBTE for computing four-phonon scattering rates and thermal conductivity

  Language:Verilog643019

• nakib/elphbolt

  A solver for the coupled and decoupled electron and phonon Boltzmann transport equations.

  Language:Fortran45712926

• Algorithm0/deepcool-digital-info

  This project provides support for Deepcool Digital for Linux OS

  Language:Python31273

• ninarina12/phononDoS_tutorial

  Code repository for a tutorial based on the "Direct prediction of phonon density of states with Euclidean neural networks"

  Language:Jupyter Notebook27126

• nanoengineering/EPW-nano

  Modified EPW code for first principles calculation of electron transport and thermoelectric property of materials, including electron-phonon scattering, defect scattering, and phonon drag.

  Language:Fortran265111

• jonaac/deep-xgboost-image-classifier

  Language:Python25219

• nguyen-group/GNNOpt

  Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures

  Language:Jupyter Notebook22304

• QuantumLab-ZY/HamEPC

  A machine learning workflow for calculating the electron-phonon coupling (EPC)

  Language:Python22294

• HeewoongNoh/DOSTransformer

  The offical source code for [2023 NeurIPS] " Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer"

  Language:Python21212

• costrouc/mse-machinelearning-notebooks

  Machine Learning Introduction for a Material Scientist

  Language:Jupyter Notebook20516

• atomicarchitects/phonax

  Language:Jupyter Notebook19412

• nguyen-group/QERaman

  A open-source program for computing the first-order resonance Raman spectroscopy based on Quantum ESPRESSO

  Language:Fortran14145

• zhongpc/drxnet

  A Python package for learning representations of battery discharge voltage profiles across diverse compositions in disordered rocksalt cathodes (DRX).

  Language:Python13101

• KRICT-DATA/SIMD

  Language:Python10111

• virtualatoms/AnisoNet

  This repository contains neccessary code to train AnisoNet, an equivariant graph neural network for predicting dielectric tensors of crystals.

  Language:Jupyter Notebook10100

• camml-lab/hubbardml

  Equivariant machine learning model for predicting self-consistent Hubbard parameters

  Language:Python9301

• Vaitesswar/Machine_Learning_for_Thermoelectric_Materials

  Language:Jupyter Notebook8102

• KAIST-ELST/Jx.jl

  The open source magnetic force theory (MFT) calculation project.

  Language:Julia7341

• shenggong1996/examining-GNN-for-crystal-periodicity

  Language:Python6101

• JianboHIT/kappamin

  A python3 code for calculations of the minimum limit to thermal conductivity

  Language:Python4100

• nguyen-group/Neural_Networks_with_Python

  This repository includes the examples for the hands-on tutorials of the artificial neural networks by using Python.

  Language:Jupyter Notebook4301

• yuanyue-liu-group/EDI

  Electron-Defect Interaction

  Language:Python410

• hgheiberger/quantum-structure-ml

  Language:Python2200

• DS-RF/Yambo_examples

  Yambo installation and examples

  Language:Shell1000

• tomokihirosawa/ML-of-thermoelectric-data

  Language:Jupyter Notebook110