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

Implementation of Retrosynthesis Prediction with Conditional Graph Logic Network

A database of reaction properties containing the atom-mapped reaction SMILES and the target properties.

Message Passing Neural Networks for Molecule Property Prediction

Uncertainty-aware prediction of chemical reaction yields with graph neural networks

Source code for "Improving Chemical Reaction Yield Prediction Using Pre-Trained Graph Neural Networks"

Code complementing our manuscript on the prediction of chemical reaction yields (https://iopscience.iop.org/article/10.1088/2632-2153/abc81d) and data augmentation strategies (https://doi.org/10.26…

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.

Code for the paper: Graph Neural Networks for Predicting Chemical Reaction Performance

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Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials …

Code and data for "Transfer learning guided the discovery of efficient perovskite oxide for alkaline water oxidation"

Code for our paper Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction (ICML 2021)

Multi-view Graph Contrastive Representation Learning for Drug-drug Interaction Prediction

Official Repository for different models based on MultiHead VGAEs.

Cross-modal generation of molecules from gene expression inputs.

Official implementation for ECCV 2022 paper "3D Instances as 1D Kernels".

The source code of the Bayesian optimization of photocatalysts research project.

Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood

🆓免费的 ChatGPT 镜像网站列表，持续更新。List of free ChatGPT mirror sites, continuously updated.

Bayesian optimization for Python

This is the code and data by Jiali Li and Pengfei Cai for the paper "Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations"