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TensorFlow 2 implementation of Wasserstein Conditional GAN with Gradient Penalty (WCGAN-GP) for synthetic data generation
Graph deep learning library for materials
Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
Helpers for working with pymatgen structure graphs.
Noise Conditional Score Networks (NeurIPS 2019, Oral)
In PyTorch Learing Neural Networks Likes CNN、BiLSTM
This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials
Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.
Graph Neural Network Library for PyTorch
NequIP is a code for building E(3)-equivariant interatomic potentials
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
A repository for implementing graph network models based on atomic structures.
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Atom2Vec: a simple way to describe atoms for machine learning
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation for SE(3) diffusion model with application to protein backbone generation
A toolbox that provides hackable building blocks for generic 1D/2D/3D UNets, in PyTorch.
DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules…
在 PyCharm 上编写 LaTeX，并实现：自动编译生成 PDF 实时查看和公式实时预览
PyTorch deep learning projects made easy.
A library for scientific machine learning and physics-informed learning
Composition-Conditioned Crystal GAN pytorch code
Free ChatGPT API Key，免费ChatGPT API，支持GPT4 API（免费），ChatGPT国内可用免费转发API，直连无需代理。可以搭配ChatBox等软件/插件使用，极大降低接口使用成本。国内即可无限制畅快聊天。
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
MLMD: a programming-free AI platform to predict and design materials