Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CrysGNN: Distilling Pre-trained Knowledge to Enhance Property Prediction for Crystalline Materials
Authors: Kishalay Das, Bidisha Samanta, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to show that with distilled knowledge from the pre-trained model, all the SOTA algorithms are able to outperform their own vanilla version with good margins. |
| Researcher Affiliation | Collaboration | Kishalay Das1, Bidisha Samanta1, Pawan Goyal1, Seung-Cheol Lee2, Satadeep Bhattacharjee2, Niloy Ganguly1,3 1 Indian Institute of Technology Kharagpur, India. 2 Indo Korea Science and Technology Center, Bangalore, India. 3 L3S, Leibniz University of Hannover, Germany. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We have released the pre-trained model along with the large dataset of 800K crystal graph which we carefully curated; so that the pretrained model can be plugged into any existing and upcoming models to enhance their prediction accuracy. ... Source code, pre-trained model, and dataset of Crys GNN is made available at https://github.com/kdmsit/crysgnn |
| Open Datasets | Yes | To this effect, we curate a new large untagged crystal dataset with 800K crystal graphs and undertake a pre-training framework (named Crys GNN) with the dataset. ... We have released the pre-trained model along with the large dataset of 800K crystal graph which we carefully curated; so that the pretrained model can be plugged into any existing and upcoming models to enhance their prediction accuracy. ... Source code, pre-trained model, and dataset of Crys GNN is made available at https://github.com/kdmsit/crysgnn |
| Dataset Splits | Yes | For each property, we trained on 80% data, validated on 10% and evaluated on 10% of the data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | We train Pψ using dataset Dt to optimize the following multitask loss: Lprop = δLMSE + (1 δ)LKD (3) ... Finally, δ signifies relative weightage between two losses, which is a hyper-parameter to be tuned on validation data. |