Selective etching of 2D materials for quantum computing devices: A computational perspective

Achieving atomic-level precision in the fabrication of quantum devices built from two-dimensional (2D) materials requires a thorough understanding of how selective etching reshapes surface chemistry, defect populations, and electronic structure at the nanoscale. This review consolidates recent computational advances in density functional theory, molecular dynamics, machine learning, and quantum chemical methods that model etching pathways in graphene, transition metal dichalcogenides, hexagonal boron nitride, and MXenes. We critically assess how these simulations predict etchant–surface reactivity, defect-driven selectivity, and post-etch changes in band structure, spin–orbit coupling, valley polarization, and coherence lifetimes. Key experimental validations, including graphene Josephson junctions, etched double quantum dots, and MoS₂ valley qubits, illustrate how reliably simulations can guide fabrication. Closed-loop machine-learning–density-functional-theory frameworks and digital twin architectures are highlighted as promising routes toward automated, adaptive manufacturing of quantum-grade 2D devices. The review also identifies persistent gaps in multi-scale modelling and dynamic environment simulation, and suggests directions for future research.