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.

EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

A Monte Carlo Tree Search (MCTS) implementation for discovering and optimizing stable intermetallic crystal structures containing uranium and f-block elements by iteratively exploring chemical space guided by formation energies and thermodynamic stability metrics from MACE energy calculations.

Variational Autoencoders for composites generation

Implement SE(3)-equivariant graph attention transformers for efficient and expressive molecular modeling in PyTorch.

Generate copper alloy compositions based on thermal conductivity

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