AI 中文总结
本文开发了一种用于W-B结构溅射及硼沉积的机器学习原子间势，经密度泛函理论数据训练，可实现大规模分子动力学模拟，揭示了表面构型、组成对溅射的影响及硼沉积形成致密层的特性，可用于W-B混合体系模拟。
AI 中文摘要
硼化（即将硼沉积到钨表面）是降低托卡马克聚变反应堆中等离子体污染（如氧）的关键技术。然而，硼原子与钨表面的精确相互作用，以及恶劣环境对这些表面的影响尚未完全明确，部分原因在于缺乏准确的原子级模拟方法和原子间势。本文中，我们开发了一种用于W-B结构溅射研究及硼沉积到钨表面的机器学习原子间势。该机器学习势基于密度泛函理论数据训练而成，可实现准确的大规模分子动力学模拟。我们的研究目标是理解硼沉积到钨表面时的行为，以及钨和硼在辐照下的溅射情况。我们观察到，表面构型/取向和表面组成均会影响溅射过程，且向钨表面沉积硼会形成致密的硼层。所开发的势对表面和体相性质均表现出良好的准确性，可用于W和B混合体系的模拟。
英文摘要
Boronization, where boron is deposited onto tungsten surfaces, is a key technique to reduce plasma contamination, such as oxygen in Tokamak fusion reactors. The exact interaction between the boron atoms and the tungsten surface, and the effect of the harsh environment on these surfaces are however not fully understood, partially due to the lack of accurate atomistic simulations and interatomic potentials. Here, we develop a machine-learned interatomic potential for sputtering studies of W-B structures and deposition of boron onto tungsten surfaces. The machine-learned potential is trained to density functional theory data and allows accurate large-scale molecular dynamics simulations. Our aim is to understand how boron behaves when deposited on tungsten and how tungsten and boron are sputtered under irradiation. We observe that both the surface configuration/orientation and the surface composition affect the sputtering, and that depositing boron onto tungsten surfaces produces a dense boron layer. The developed potential shows good accuracy for both surface and bulk properties and can be used for simulations of mixed W and B systems.
