High-Throughput Computational Discovery of Inverted Resistive Switching in Two-Dimensional Materials

Abstract

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.