Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach

An Vuong, Minh-Hao Van, Chen Zhao, Xintao Wu

Keywords: Materials Stacking, Property Prediction, Multimodal Learning

TL;DR: This work proposes a multimodal learning framework to predict properties of stacked homo- and hetero-bilayer 2D materials without requiring costly DFT calculations

Abstract: AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions. Bilayer 2D material stacking is essential for exploring new materials with novel functions and inherent phenomena, enabling the creation of new 2D bilayers for diverse real-world applications. Research on bilayer vdWs materials has made significant progress from experimental and computational perspectives. Various bilayer materials have been successfully synthesized experimentally and the increasing utilization of high-throughput computing technology has constructed several computational two-dimensional materials databases. However, the use of AI to model bilayer stacking and predict new properties remains underexplored, necessitating further research studies. In this work, we propose a novel multimodal learning approach to study the interfaces between dissimilar materials that jointly enable new or multiple functions, and to predict new properties arising from the vertical integration (stacking) of different functional material layers under given configurations. Comprehensive experiments demonstrate the effectiveness and efficiency of our approach compared to baseline methods. Our code is available at https://tinyurl.com/bimat-ml-code.

Submission Track: Full Paper

Submission Category: AI-Guided Design

Submission Number: 17