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 | Conference PDF | Archive PDF | Plain Text | 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. |