High-efficient low-cost characterization of materials properties using domain-knowledge-guided self-supervised learning

Abstract

Modern AI-assisted approaches have helped material scientists revolutionize their abilities to better understand the properties of materials. However, current machine learning (ML) models would perform awful for materials with a lengthy production window and a complex testing procedure because only a limited amount of data can be produced to feed the model. Here, we introduce self-supervised learning (SSL) to address the issue of lacking labeled data in material characterization. We propose a generalized SSL-based framework with domain knowledge and demonstrate its robustness to predict the properties of a candidate material with the fewest data. Our numerical results show that the performance of the proposed SSL model can match the commonly-used supervised learning (SL) model with only 5 % of data, and the SSL model is also proven with ease of implementation. Our study paves the way to expand further the usability of ML tools for a broader material science community.