21 citations · 31 across the 8 of their papers we have counts for
5 papers · 1 filter
Model Agnostic Graph Prompt Learning for Crystal Property Prediction
Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2
Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into t…
Latent Diffusion Pretraining for Crystal Property Prediction
Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri +2
Fast and accurate prediction of crystal properties is a central challenge in new materials design. Graph neural networks and Transformer-based models have emerged as powerful tools…
LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation
Subhojyoti Khastagir, Kishalay Das, Pawan Goyal +3
Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language mo…
Periodic Materials Generation using Text-Guided Joint Diffusion Model
Kishalay Das, Subhojyoti Khastagir, Pawan Goyal +3
Equivariant diffusion models have emerged as the prevailing approach for generating novel crystal materials due to their ability to leverage the physical symmetries of periodic mat…
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
Kishalay Das, Bidisha Samanta, Pawan Goyal +3
In recent years, graph neural network (GNN) based approaches have emerged as a powerful technique to encode complex topological structure of crystal materials in an enriched repres…