Non-Euclidean interfaces decode the continuous landscape of graphene-induced surface reconstructions

TL;DR

This paper introduces a novel approach using non-Euclidean, curved graphene-copper interfaces combined with advanced microscopy and machine learning to decode and control surface reconstructions in 2D material heterostructures.

Contribution

It presents a new methodology integrating multimodal microscopy, deep learning, and thermodynamic modeling to understand complex interfacial reconstructions on non-flat surfaces.

Findings

Reconstruction governed by a unified thermodynamic mechanism

High-index facets correspond to specific energy minima

Non-Euclidean topologies enable continuous landscape exploration

Abstract

Interfacial reconstruction between two-dimensional (2D) materials and metal substrates fundamentally governs heterostructure properties, yet conventional flat substrates fail to capture the continuous crystallographic landscape. Here, we overcome this topological limitation using non-Euclidean interfaces-curved 2D graphene-copper surfaces as a model system-to traverse the infinite spectrum of lattice orientations. By integrating multimodal microscopy with a deep-learning-enhanced dimensional upscaling framework, we translate 2D scanning electron microscopy (SEM) contrast into quantitative three-dimensional (3D) morphologies with accurate facet identification. Coupling these observations with machine-learning-assisted density functional theory, we demonstrate that reconstruction is governed by a unified thermodynamic mechanism where high-index facets correspond to specific local minima in the surface energy landscape. This work resolves the long-standing complexity of graphene-copper faceting and establishes non-Euclidean surface topologies as a generalizable paradigm for decoding and controlling interfacial reconstruction in diverse metal-2D material systems.