chc273

deepmd-kit

A deep learning package for many-body potential energy representation and molecular dynamics

gnn_mrs2022

MRS2022 Tutorial on Graph Neural Network

matsim

Material simulation toolkits and tutorials

mep

Minimum energy path tools for atomistic simulations

ml_material_tutorials

Tutorials of applying machine learning models to materials

QuantumResources

A collection of resources for Quantum Computing

m3gnet

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.

maml

Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

snap

Repository for spectral neighbor analysis potential (SNAP) model development.

chc273/mep

Minimum energy path tools for atomistic simulations

chc273/ml_material_tutorials

Tutorials of applying machine learning models to materials

chc273/matsim

Material simulation toolkits and tutorials

chc273/chc273.github.io

website

chc273/deepmd-kit

A deep learning package for many-body potential energy representation and molecular dynamics

chc273/gnn_mrs2022

MRS2022 Tutorial on Graph Neural Network

chc273/QuantumResources

A collection of resources for Quantum Computing

chc273/ASE_ANI

ANI-1 neural net potential with python interface (ASE)

chc273/atomsk

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

chc273/carcnn

Convolutional neural network powered self-driving car model

chc273/CASMcode

First-principles statistical mechanical software for the study of multi-component crystalline solids

chc273/chc273

chc273/conda

Specifying a conda environment with `environment.yml`

chc273/llm.c

LLM training in simple, raw C/CUDA

chc273/Materials-Databases

chc273/materialsdata

A collection of materials data for machine learning purposes

chc273/mdcluster

Density-based clustering of trajectories

chc273/miworkshop

Materials for Workshop on Materials Informatics

chc273/mvl_models

MAVRL models

chc273/pymatgen

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project.

chc273/pymatgen-db

Pymatgen-db provides an addon to the Python Materials Genomics (pymatgen) library (https://pypi.python.org/pypi/pymatgen) that allows the creation of Materials Project-style databases for management of materials data.

chc273/semantic-kernel

Integrate cutting-edge LLM technology quickly and easily into your apps

chc273/SIMPLE-NN

SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE – version Neural Network)

chc273/Supercon

Data used in "Machine learning modeling of superconducting critical temperature" paper

chc273/tensorflow

An Open Source Machine Learning Framework for Everyone

chc273/torchani

Accurate Neural Network Potential on PyTorch

chc273/tutorials