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Satadeep Bhattacharjee

4 accepted papers

2025

LLM Meets Diffusion: A Hybrid Framework for Crystal Material Generation

NeurIPS 2025poster

Recent advances in generative modeling have shown significant promise in designing novel periodic crystal structures. Existing approaches typically rely on either large language models (LLMs) or equivariant denoising models, each with complementary strengths: LLMs excel at handling discrete atomic t…

Cited by 0SourcecodeScholar
2025

Periodic Materials Generation using Text-Guided Joint Diffusion Model

ICLR 2025poster

Equivariant diffusion models have emerged as the prevailing approach for generat- ing novel crystal materials due to their ability to leverage the physical symmetries of periodic material structures. However, current models do not effectively learn the joint distribution of atom types, fractional co…

2023

CrysGNN: Distilling Pre-trained Knowledge to Enhance Property Prediction for Crystalline Materials

AAAI 2023technical

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 repre- sentation space. These models are often supervised in nature and using the property-specific training data, learn relat…

2023

CrysMMNet: Multimodal Representation for Crystal Property Prediction

UAI 2023poster

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 modality of crystal data i.e crystal graph structure, where they construct multi-graph by establishing edges between nearby a…