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100 Days of ML Coding
100-Days-Of-ML-Code中文版
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ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.
A paper collection about automated graph learning
Paper list for equivariant neural network
Papers about graph transformers.
Awesome resources on normalizing flows.
A Python implementation of global optimization with gaussian processes.
An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]
Crystal graph convolutional neural networks for predicting material properties.
A flexible framework of neural networks for deep learning
Pretrained universal neural network potential for charge-informed atomistic modeling
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.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
A JAX library for Density Functional Theory.
Jupyter notebooks and data for our Chemistry of Materials article "Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors"
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
深度学习、强化学习、模仿学习与机器人
A deep learning package for many-body potential energy representation and molecular dynamics
This repository contains implementations and illustrative code to accompany DeepMind publications
Deep learning quantum Monte Carlo for electrons in real space
A library combining solid quantum Monte Carlo and neural network.
Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation
Interactive Jupyter Notebooks for learning the fundamentals of Density-Functional Theory (DFT)
Routines for Radial Integration of Dirac, Schrödinger, and Poisson Equations
Hitchhiker's Guide to Deep Learning for Computational Scientists
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.