BDIP-Net：用于双层材料性能预测的双交互图学习方法

中文摘要 AI 辅助

堆叠双层材料具有丰富的依赖堆叠方式的性能，这种性能由层内强化学键与层间弱范德华相互作用的相互作用所驱动。此类材料的计算发现颇具挑战性，因为准确的结构生成通常依赖于昂贵的基于DFT的优化，而现有机器学习模型在性能预测过程中往往无法明确区分不同类型的相互作用。为应对这些挑战，我们提出了一种用于堆叠双层材料高效构建与性能预测的机器学习框架。该框架采用基于MatterSim-D3的结构优化工作流，从单层构建单元和堆叠构型生成DFT质量的双层结构，同时大幅降低计算成本。在性能预测方面，我们引入了BDIP-Net（Bilayer Dual-Interaction Potential Network，双层双交互势能网络），这是一种图神经网络，它通过交互特定的势能表示和自适应消息融合来明确建模层内与层间相互作用。我们在BiDB、HetDB和SAMBA数据集上评估了该框架，这些数据集涵盖同双层、异双层和扭曲双层体系。结果表明，基于MatterSim-D3的工作流可紧密复现DFT-PBE-D3优化的结构，而BDIP-Net在双层材料性能预测任务中始终优于现有图神经网络和基于势能的方法。

英文摘要

Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions. The computational discovery of such materials is challenging because accurate structure generation typically relies on expensive DFT-based optimization, while existing machine-learning models often fail to explicitly distinguish different interaction types during property prediction. To address these challenges, we propose a machine-learning framework for efficient construction and property prediction of stacked bilayer materials. The framework employs a MatterSim-D3-based structural optimization workflow to generate DFT-quality bilayer structures from monolayer building blocks and stacking configurations at substantially reduced computational cost. For property prediction, we introduce BDIP-Net (Bilayer Dual-Interaction Potential Network), a graph neural network that explicitly models intra-layer and inter-layer interactions through interaction-specific potential representations and adaptive message fusion. We evaluate the proposed framework on BiDB, HetDB, and SAMBA, encompassing homobilayers, heterobilayers, and twisted bilayer systems. Results show that the MatterSim-D3-based workflow closely reproduces DFT-PBE-D3 optimized structures, while BDIP-Net consistently outperforms existing graph neural network and potential-based approaches for bilayer property prediction.

发表机构

• University of Arkansas（阿肯色大学）
• Baylor University（贝勒大学）

机构由 AI 辅助整理，请以论文原文为准。