ryan-dai Goto Github PK

aicrystallographer

Here, we will upload our deep/machine learning models and 'workflows' (such as AtomNet, DefectNet, SymmetryNet, etc) that aid in automated analysis of atomically resolved images

algorithm

算法导论python实现

asuagaveguide

atomic2field

Codes for translating structural defects to atomic properties

atomsk

Atomsk: A Tool For Manipulating And Converting Atomic Data Files -

avogadro-packmol-extension

An Avogadro plugin with a gui to create packmol input files and run packmol from within Avogadro.

awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

awesome_deep_learning_interpretability

深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)

cg-method

https://doi.org/10.1016/j.ijmecsci.2020.106112

computationally-efficient-design-of-directionally-compliant-metamaterials

Software to design directionally compliant metamaterials

coursera-aml-docker

Docker container with Jupyter Environment for Coursera "Advanced Machine Learning" specialization.

deep-learning

PyTorch Tutorial

deep-learning-for-indentation

Extraction of mechanical properties of materials through deep learning from instrumented indentation

dftbparaopt

DFTB parameter optimization tools

elate

ELATE: Elastic tensor analysis

entityembedding-working_example

This repository contains a notebook demonstrating a practical implementation of the so-called Entity Embedding for Encoding Categorical Features for Training a Neural Network.

geant4p11_crystal_diffraction

geant4p11_gun_particle_source

gnuplot-palettes

Color palettes for gnuplot

intro-to-dl

Resources for "Introduction to Deep Learning" course.

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

lammps

Public development project of the LAMMPS MD software package

lammps_graphene

Lammps tutorial: graphene simulations

loss-landscape

Code for visualizing the loss landscape of neural nets

metamaterials-inverse-design

ml_for_kirigami_design

Python package to model and to perform topology optimization for graphene kirigami using deep learning

molecular-dynamics-simulation

Sample codes for my book on molecular dynamics simulation

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

polygraphene

Generation of polycrystalline graphene models

polypy

polygon (ring network) discovery from XYZ files