JYNi16
Research Fellow at NTU. Condensed Matter Physics and Machine Learning.
Nanyang Technological UniversitySingapore
JYNi16's Stars
PaulChern/LINVARIANT
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
yzhuqici/learn_quantum_properties_from_local_correlation
guaguabujianle/DeepRelax
Shen-Group/DeepRelax
longyangking/ml_topology_non_Abelian_braiding
mir-group/allegro
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
PKUFlyingPig/cs-self-learning
计算机自学指南
lukas-weber/Carlo.jl
Monte Carlo framework that provides MPI parallelization, checkpointing and statistical postprocessing in an algorithm-agnostic way.
cnmozzie/Ising_Model_TRG
an implementation of the tensor renormalization group (TRG) for two dimensional Ising model
greydanus/hamiltonian-nn
Code for our paper "Hamiltonian Neural Networks"
geremiam/wannier-hamiltonians
Utilities for computing Wilson loops and Wannier Hamiltonians for both fermionic and bosonic systems
kuansenlin/nested_and_spin_resolved_Wilson_loop
PythTB-based package for nested Wilson loop and spin-resolved tight-binding calculations
ziyanzzhu/HubbardNet
Deep neural network-based solution to the ground and excited states of 1D and 2D Bose-Hubbard model
qtm-iisc/Twister
stcarr/kp_tblg
A relaxed kp model of twisted bilayer graphene
zihaophys/twisted_bilayer_graphene
Electronic structure calculation of twisted bilayer graphene
quantum-tinkerer/qsymm
A mirror of https://gitlab.kwant-project.org/qt/qsymm
issp-center-dev/HPhi
Quantum Lattice Model Simulator Package
krishnapitike/FERAM-noisyopt
Python script to optimize effective hamiltonian parameters for ferroelectric perovskite oxides
wannier-berri/wannier-berri
Advanced tool for Wannier interpolation and integration of k-space integrals
zhihao-huang-07/Code4Paper_Unsupervised_Learning_of_Non_Hermitian_Topological_Phases
reproduce some results of arXiv:2010.14516
Xiaoxun-Gong/DeepH-E3
netket/netket
Machine learning algorithms for many-body quantum systems
L1aoXingyu/code-of-learn-deep-learning-with-pytorch
This is code of book "Learn Deep Learning with PyTorch"
TDIV/TBM3
Tight-Binding Modeling for Materials at Mesoscale
google/TensorNetwork
A library for easy and efficient manipulation of tensor networks.
EverettYou/Mathematica-for-physics
Mathematica Packages for Physicists
deepmodeling/DeePTB
DeePTB: A deep learning package for tight-binding approach with ab initio accuracy.
DerWeh/gftools
Collection of commonly used Green's functions and utilities