danielathk Goto Github PK

100-days-of-ml-code

100 Days of ML Coding

100-days-of-ml-code-chinese

100-Days-Of-ML-Code中文版

ai2bmd

AI-powered ab initio biomolecular dynamics simulation

aml

A... M... L...

asap

ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.

awesome-auto-graph-learning

A paper collection about automated graph learning

awesome-equivariant-network

Paper list for equivariant neural network

awesome-graph-transformer

Papers about graph transformers.

awesome-normalizing-flows

Awesome resources on normalizing flows.

awesome-pretrain-on-molecules

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

cdvae

An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

chainer

A flexible framework of neural networks for deep learning

chgnet

Pretrained universal neural network potential for charge-informed atomistic modeling

computationalphysics2

Advanced course in Computational Physics, see texbook at http://compphysics.github.io/ComputationalPhysics2/doc/LectureNotes/_build/html/ with an emphasis on computational quantum mechanics, machine learning and quantum computing.

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

d4ft

A JAX library for Density Functional Theory.

data-driven-first-principles-methods-for-the-study-and-design-of-alkali-superionic-conductors

Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors"

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

deep-learning-topics

deeplearning

深度学习、强化学习、模仿学习与机器人

deepmd-kit

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

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deepqmc

Deep learning quantum Monte Carlo for electrons in real space

deepsolid

A library combining solid quantum Monte Carlo and neural network.

deeptime

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

dffs

dftatom

Routines for Radial Integration of Dirac, Schrödinger, and Poisson Equations

dftworks