PaddleMaterials is a data-mechanism dual-driven, foundation model development and deployment, end to end toolkit based on PaddlePaddle deep learning framework for materials science.

QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking

Variational Autoencoders for composites generation

Generate copper alloy compositions based on thermal conductivity

Fork of the Ceder Group's Text-Mining Synthesis packages

🧬 Discover and utilize QuantumChem-200K, a dataset of 200,000 organic molecules with key quantum-chemical properties for research and modeling.