二维材料中倒置阻变的高通量计算发现
High-Throughput Computational Discovery of Inverted Resistive Switching in Two-Dimensional Materials
- Science, Mathematics and Technology (SMT) Cluster, Singapore University of Technology and Design（新加坡科技设计大学）
- Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR)（科学技术研究局）
- School of Information Science and Technology, Northwest University（西北大学）
- School of Physics and Optoelectronics, Xiangtan University（湘潭大学）
- Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Jiangsu Physical Science Research Center, Nanjing University（南京大学）
- Quantum Innovation Centre (Q. InC), Agency for Science Technology and Research (A*STAR)（科学技术研究局）
- Department of Mechanical Engineering, National University of Singapore（新加坡国立大学）
- Department of Materials Science and Engineering, National University of Singapore（新加坡国立大学）
- Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过高通量计算框架筛选2900种二维材料，发现17种可实现倒置阻变的原子忆阻器候选材料，拓展了其设计范围，确立互补阻变的设计范式。
AI中文摘要：
原子忆阻器（Atomristor）是基于二维（2D）单分子层的非易失性阻变器件，是高能效存储器和神经形态计算的有前途构建块。然而，其设计仍局限于MoS₂和h-BN等少数材料，限制了功能多样性和设计灵活性。本文结合密度泛函理论、机器学习分子动力学和量子输运模拟的高通量计算框架，筛选了约2900种可剥离单分子层的空位介导阻变，确定了两类机制不同的17种热稳定候选材料。在GaS等1类单分子层中，本征空位处的金（Au）吸附引入了导电态，使绝缘单分子层从高阻态（HRS）切换至低阻态（LRS）。由离子键合的金属氧卤化物和硝基卤化物（如BiOCl）组成的2类单分子层表现出此前未报道的倒置阻变：空位释放的电子离域并将费米能级推入导带，使器件天然处于LRS；Au吸附使这些载流子重新定域，费米能级回到带隙中，驱动LRS切换至HRS。量子输运模拟证实了两种机制，而迁移势垒计算表明，电极-二维材料间距是控制Au迁移和电阻窗口的关键参数。这些发现拓展了原子忆阻器的范围，并确立了互补阻变作为多功能存储器和神经形态硬件的设计范式。
英文摘要：
Atomristors, non-volatile resistive switching devices based on two-dimensional (2D) monolayers, are promising building blocks for energy-efficient memory and neuromorphic computing. However, their design remains restricted to a few materials such as MoS2 and h-BN, limiting functional diversity and design flexibility. Here, a high-throughput computational framework combining density functional theory, machine-learning molecular dynamics, and quantum transport simulations screens about 2,900 exfoliable monolayers for vacancy-mediated resistive switching, identifying 17 thermally stable candidates in two mechanistically distinct classes. In Class 1 monolayers, such as GaS, Au adsorption at the native vacancy introduces conducting states, switching the insulating monolayer from a high- to a low-resistance state (HRS-to-LRS). Class 2 monolayers, comprising ionically bonded metal oxyhalides and nitrohalides such as BiOCl, exhibit previously unreported inverted switching. Vacancy-released electrons delocalize and push the Fermi level into the conduction band, placing the device natively in the LRS; Au adsorption re-localizes these carriers and returns the Fermi level to the gap, driving LRS-to-HRS switching. Quantum transport simulations confirm both mechanisms, while migration-barrier calculations identify the electrode-2D separation as a key parameter governing Au migration and the resistance window. These findings expand the atomristor landscape and establish complementary switching as a design paradigm for multifunctional memory and neuromorphic hardware.
