21 citations · 31 across the 8 of their papers we have counts for
9 papers
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
Kishalay Das
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal p…
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…
CrysMMNet: Multimodal Representation for Crystal Property Prediction
Kishalay Das, Pawan Goyal, Seung-Cheol Lee +2
Machine Learning models have emerged as a powerful tool for fast and accurate prediction of different crystalline properties. Exiting state-of-the-art models rely on a single modal…
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…
CrysXPP:An Explainable Property Predictor for Crystalline Materials
Kishalay Das, Bidisha Samanta, Pawan Goyal +3
We present a deep-learning framework, CrysXPP, to allow rapid prediction of electronic, magnetic and elastic properties of a wide range of materials with reasonable precision. Alth…
Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs
Sambaran Bandyopadhyay, Kishalay Das, M. Narasimha Murty
Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (G…