21 citations · 31 across the 8 of their papers we have counts for
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cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2023★ 9 cited
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…
cond-mat.mtrl-sci2021
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…