Open-source AI infrastructure for materials science
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
Python package designed to run atomistic Monte Carlo simulations.
Variational Autoencoders for composites generation
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.
Implement SE(3)-equivariant graph attention transformers for efficient and expressive molecular modeling in PyTorch.
Generate copper alloy compositions based on thermal conductivity
Data, trained XGBoost surrogate, and code for quantifying symmetry breaking as a design variable for giant altermagnetic spin splitting (MSBI)
Fork of the Ceder Group's Text-Mining Synthesis packages
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