用多模态学习预测双层二维材料堆叠后的性质，加速新材料发现。
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
- 融合多种数据模态建模异质材料界面
- 在多种堆叠配置下准确预测新物性
- 适合材料设计与计算物理研究者
人工智能在科学领域的应用是推动材料科学发展的关键方向，旨在加速材料发现并实现高精度性质预测。双层二维材料的堆叠对于探索具有新功能和内在现象的新材料至关重要，可为实际应用构建新型二维双层结构。实验与计算研究已在范德华双层材料领域取得显著进展，已有多种双层材料被成功合成，且高通量计算技术的发展建立了多个二维材料数据库。然而，利用人工智能建模双层堆叠并预测新性质的研究仍不充分，亟需深入探索。本文提出一种新颖的多模态学习方法，用于研究异质材料界面协同作用带来的多功能性，并在给定堆叠构型下预测垂直集成不同功能层所产生的新性质。大量实验表明，该方法在效果与效率上均优于基线模型。代码已开源：https://github.com/AnVuong123/bimat_ml。
原文摘要 · Abstract (English)
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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