含水环境下纳米多孔石墨烯的机器学习带隙预测
Machine Learning Bandgap Prediction of Nanoporous Graphenes with Water
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中文总结 AI 辅助
结合DFT、AIMD与可解释ML，开发SOAP基ML模型，揭示水的结构与性质对NPG带隙的调控机制，实现带隙的精准预测与物理解释。
中文摘要 AI 辅助
在纳米结构表面或内部受限的水的结构与动力学行为是诸多领域的核心研究主题，涵盖从生物学到碳纳米结构等新兴电子领域。具有特定拓扑结构的周期性纳米级孔洞的纳米多孔石墨烯（NPG）已成为碳基纳米电子学领域极具前景的材料，但其与环境水的相互作用仍知之甚少。本文中，我们结合密度泛函理论（DFT）、从头算分子动力学（AIMD）和可解释机器学习（ML），揭示水如何调控NPG中的量子输运。根据局部水合结构的不同，NPG和氮掺杂杂化（h-NPG）体系的带隙变化超过两倍。为揭示潜在机制，我们开发了基于原子位置平滑重叠（SOAP）的黑箱模型和物理启发灰箱机器学习模型。高斯过程回归模型达到接近DFT的精度，同时支持物理解释。分析确定水偶极取向、水-基底距离、水几何中心以及带分辨偶极矩是调控NPG和h-NPG体系带隙的主导因素。
英文摘要
The structure and dynamical behavior of water confined at or within nanostructures is a topic central to many fields, from biology to emerging electronics such as carbon nanostructures. Nanoporous graphene (NPG) containing periodic nanoscale pores with specific topologies has emerged as a promising material in carbon-based nanoelectronics; however, its interaction with ambient water remains poorly understood. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and interpretable machine learning (ML) to reveal how water controls quantum transport in NPGs. Depending on the local hydration structure, the bandgap varies by more than a factor of two across NPG and nitrogen-doped hybrid (h-NPG) systems. To uncover the underlying mechanism, we develop Smooth Overlap of Atomic Positions (SOAP)-based black-box and physics-informed grey-box ML models. The Gaussian process regression model achieves near-DFT accuracy while enabling physical interpretation. Analysis identifies water dipole orientation, water-substrate distance, water center-of-geometry, and ribbon-resolved dipole moments as the dominant factors controlling bandgap modulation across NPG and h-NPG systems.
