Superionic Conductor Materials Screening & Phase Prediction

Project Introduction

Using a small amount of data on superionic conductor materials in the Li-N-S system, the general potential model MatterSim is fine-tuned, and then the fine-tuned model is used to screen the generation model MatterGen to obtain stable materials in the Li-N-S system.

Personal Contributions

Constructed an active learning loop to iteratively update the model and data:

• Used a small amount of labelled superionic conductor material data to train and test MatterSim.
• Generated unlabelled superionic conductor structures using MatterGen.
• Loaded MatterSim as a structure prediction model and used the Query By Committee method to obtain its uncertainty.
• Performed DFT calculations on the data with the highest uncertainty and added it to the training data for the next round of training.

Achievements

• Selected for SJTU-Global College Research Internship Program 2025.