Issue

Vol. 18 (2026)

2D Materials Powering Neuromorphic Intelligence

https://doi.org/10.1007/s40820-026-02253-1
Jamal Kazmi
Department of Physics, College of Sciences, Shanghai University, Shanghai 200444, People’s Republic of China
Waqas Ahmad
Laboratory of 2D Optoelectronics and Nanoelectronics (L2DON), State Key Laboratory of Quantum and Functional Materials, Department of Materials Science and Engineering, Southern University of Science and Technology, 1088 Xueyuan Vlvd, Shenzhen 518000, People’s Republic of China
Muhammad Naqi
School of Engineering, Liverpool John Moores University, Liverpool L3 3AF, UK
Yawar Abbas
Electronic and Nanoscale Engineering Division, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, Scotland, UK
Aumber Abbas
Department of Mechanical Engineering and Interdisciplinary Research Center for Hydrogen Technologies & Carbon Management, King Fahd University of Petroleum and Minerals (KFUPM), 31261 Dhahran, Saudi Arabia
Peijian Wang
Key Laboratory of Carbon Materials of Zhejiang Province, College of Chemistry and Materials Engineering, Wenzhou University, Wenzhou 325035, People’s Republic of China
Mohd Ambri Mohamed
Institute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia
Federico Rosei
Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via Gorgerin 1, 34127 Trieste, Italy
Zhiming M. Wang
The School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, People’s Republic of China
Hongwei Song
Department of Physics, College of Sciences, Shanghai University, Shanghai 200444, People’s Republic of China

Corresponding Author: Hongwei Song

songhw@jlu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 413

Abstract

The exponential demand for energy-efficient and adaptive computing architectures drives the evolution of artificial intelligence (AI) and machine learning (ML). Neuromorphic computing, inspired by biological neural networks, overcomes the limitations of traditional von Neumann architectures, including high energy consumption and limited scalability. The introduction of two-dimensional (2D) materials, such as transition metal dichalcogenides, hexagonal boron nitride, black phosphorus, and tellurene, enables neuromorphic devices with unprecedented control over electronic and optoelectronic properties. These materials exhibit atomic-scale thickness, high carrier mobility, and tunable bandgaps, facilitating synaptic behaviours such as spike-timing-dependent plasticity and paired-pulse facilitation. This review describes the integration of 2D materials into neuromorphic systems, highlighting applications in wearable electronics, brain–machine interfaces, and quantum neuromorphic platforms. In wearable and edge computing, 2D-based devices enable localized, ultra-low-power data processing. In brain–machine interfaces, they enhance signal transduction and neural interfacing. Quantum effects in 2D materials further enable hybrid quantum–classical neuromorphic architectures for high-dimensional computational tasks. Despite significant advances, challenges in reproducibility, scalability, and stability remain. Addressing these limitations through innovations in synthesis and defect passivation is essential for practical application. This review underscores the transformative potential of 2D-material-based neuromorphic computing for energy-efficient AI.


Highlights:
1 Two-dimensional materials enable energy-efficient neuromorphic computing through gate tuneable bandgaps, fast switching kinetics, and compatibility with spiking neural networks for adaptive learning.
2 Emerging memory devices leveraging 2D materials—including resistive, ferroelectric, and phase-change systems—mimic synaptic plasticity to revolutionize neuromorphic architectures.
3 Future advances in thin-film synthesis, defect engineering, and quantum-inspired designs will unlock scalable, sustainable 2D neuromorphic systems for healthcare, edge AI, and quantum computing.

Keywords

2D neuromorphic computing Machine-learning integration Artificial synaptic devices Transition metal dichalcogenides Quantum-inspired neuromorphics
Kazmi, Jamal, Waqas Ahmad, Muhammad Naqi, Yawar Abbas, Aumber Abbas, Peijian Wang, Mohd Ambri Mohamed, Federico Rosei, Zhiming M. Wang, and Hongwei Song. 2026. “2D Materials Powering Neuromorphic Intelligence”. Nano-Micro Letters 18 (June):413. https://doi.org/10.1007/s40820-026-02253-1.
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