CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials

14 January 2023

Papers citing "CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials"

7 / 7 papers shown

Title
Siamese Foundation Models for Crystal Structure Prediction Liming Wu Wenbing Huang Rui Jiao Jianxing Huang Liwei Liu ... Hao Sun Yang Liu F. Sun Yuxiang Ren J. Wen 45 0 0 13 Mar 2025
Periodic Materials Generation using Text-Guided Joint Diffusion Model Kishalay Das Subhojyoti Khastagir Pawan Goyal Seung-Cheol Lee S. Bhattacharjee Niloy Ganguly DiffM 32 0 0 01 Mar 2025
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction Shrimon Mukherjee Madhusudan Ghosh Partha Basuchowdhuri 32 0 0 07 Sep 2024
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning Ulrik Friis-Jensen Frederik L. Johansen A. Anker Erik B. Dam Kirsten M. Ø. Jensen Raghavendra Selvan AI4CE 32 3 0 20 Feb 2024
M $^2$ Hub: Unlocking the Potential of Machine Learning for Materials Discovery Yuanqi Du Yingheng Wang Yin-Hua Huang Jianan Canal Li Yanqiao Zhu T. Xie Chenru Duan J. Gregoire Carla P. Gomes 32 6 0 14 Jun 2023
CrysMMNet: Multimodal Representation for Crystal Property Prediction Kishalay Das Pawan Goyal Seung-Cheol Lee S. Bhattacharjee Niloy Ganguly 13 11 0 09 Jun 2023
Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie Xiang Fu O. Ganea Regina Barzilay Tommi Jaakkola DiffM BDL 206 230 0 12 Oct 2021