The code for "Graph Diffusion Transformer for Multi-Conditional Molecular Generation"

Hierarchical Generation of Molecular Graphs using Structural Motifs

Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization (AAAI 21')

使用DGL和pytorch实现metapath2vec

Representation learning on large graphs using stochastic graph convolutions.

本人的科研经验

graph neural networks, information theory, AI for Sciences

Learning Intents behind Interactions with Knowledge Graph for Recommendation, WWW2021

The code of paper LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence. Zhihao Shi, Xize Liang, Jie Wang. ICLR 2023.

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

Platform to experiment with the AI Software Engineer. Terminal based. NOTE: Very different from https://gptengineer.app

The official source code for "Conditional Graph Information Bottleneck for Molecular Relational Learning".

Python implementation of the paper: Self-Supervised Graph Information Bottleneck for Multi-View Molecular Embedding Learning

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

reimplement TransE Mode

An Open-Source Package for Knowledge Embedding (KE)

TechGPT: Technology-Oriented Generative Pretrained Transformer

Repository for Text2Mol: Cross-Modal Molecular Retrieval with Natural Language Queries

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

Robust representation of semantically constrained graphs, in particular for molecules in chemistry

Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022)

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

Artificial Intelligence Research for Science (AIRS)

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型