Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach

• URL: http://arxiv.org/abs/2606.01012v1
• Date: Sun, 31 May 2026 05:04:03 GMT
• Title: Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
• Abstract summary: Bilayer 2D material stacking is essential for exploring new materials with novel functions and inherent phenomena.<n>We propose a novel multimodal learning approach to study the interfaces between dissimilar materials that jointly enable new or multiple functions.
• Score: 22.965734443330707
• License: http://creativecommons.org/licenses/by/4.0/
• 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 synthe sized experimentally and the increasing utilization of high-throughput computing technology has con structed 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://github.com/AnVuong123/bimat ml.

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