Pinned Repositories
benchmarking_graph
DiSCoMaT
FNO-StressStrain
Using Fourier Neural Operator to measure the physical fields for a mode-I failure of 2D composites
LGNN
Lagrangian Graph Neural Network
MaScQA
MatSciBERT
Material Science Aware Language Model
MDBENCHGNN
ML-DOS
Implementation of unsupervised GNN (GraphSAGE) to generate node representations for disordered systems. OPTICS clustering of obtained node embeddings reveals structure-dynamics correlation.
Physics-Informed-GNNs
# Graph Neural Networks
rigid_body_dynamics_graph
Multiphysics and Multiscale Mechanics Research Group (M3RG), IIT Delhi's Repositories
M3RG-IITD/MatSciBERT
Material Science Aware Language Model
M3RG-IITD/rigid_body_dynamics_graph
M3RG-IITD/benchmarking_graph
M3RG-IITD/FNO-StressStrain
Using Fourier Neural Operator to measure the physical fields for a mode-I failure of 2D composites
M3RG-IITD/MDBENCHGNN
M3RG-IITD/ML-DOS
Implementation of unsupervised GNN (GraphSAGE) to generate node representations for disordered systems. OPTICS clustering of obtained node embeddings reveals structure-dynamics correlation.
M3RG-IITD/DiSCoMaT
M3RG-IITD/MaScQA
M3RG-IITD/Physics-Informed-GNNs
# Graph Neural Networks
M3RG-IITD/cod-bench
We present cod-bench containing 12 operators and 10 datasets.
M3RG-IITD/LGNN
Lagrangian Graph Neural Network
M3RG-IITD/SRIMP_Hardness
M3RG-IITD/StrainEvolution
M3RG-IITD/StriderNET
Striding through the energy landscape
M3RG-IITD/BroGNet
M3RG-IITD/MatSci-IE-Challenges
M3RG-IITD/MatSci-LLM
M3RG-IITD/ML-Conductivity
M3RG-IITD/structure_factor
The repository sharing the codes for meta data analysis of glass science research papers published in Elsevier journals
M3RG-IITD/Cementron
M3RG-IITD/afm-ai
M3RG-IITD/chgs-ai
M3RG-IITD/graph_neural_ODE
M3RG-IITD/HGNN
Hamiltonian Graph Neural Network
M3RG-IITD/matsciml
Open MatSci ML Toolkit is a single framework for prototyping and scaling out deep learning models for materials discovery, built on top of OpenCatalyst, PyTorch Lightning, and the Deep Graph Library.
M3RG-IITD/PeriDyn
M3RG-IITD/Understanding-the-Compositional-Control
Understanding the Compositional Control on Electrical, Mechanical, Optical, and Physical Properties of Inorganic Glasses with Interpretable Machine Learning