AI 中文总结
该研究提出结合机器学习电子结构模型与量子输运求解器的从头算方法，可加速模拟含超2万原子的半导体器件，揭示了MoS2与HfO2或Al2O3界面附近低配位金属原子对电子电流的影响。
AI 中文摘要
我们提出了一种新的从头算方法来模拟半导体器件，该方法将可扩展的机器学习（ML）电子结构模型与先进的量子输运（QT）求解器相结合。所开发的框架相比密度泛函理论实现了10000倍的加速，可生成包含超过20000个原子的器件的哈密顿矩阵，同时具备高预测精度。我们利用其独特特性研究了MoS2/氧化物样品和单层MoS2场效应晶体管，其中周围的氧化物层（此处为HfO2或Al2O3）被明确纳入QT域。特别地，我们揭示了半导体-氧化物界面附近低配位金属原子（Hf或Al）的存在会显著影响电子电流的大小及其在MoS2中的传播。
英文摘要
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver. The developed framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms, while offering high prediction accuracy. We use its unique features to investigate MoS2/oxide samples and single-layer MoS2 field-effect transistors, where the surrounding oxide layers, here, HfO2 or Al2O3, are explicitly included into the QT domain. In particular, we reveal that the presence of undercoordinated metal atoms (Hf or Al) close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.
