Emerging Nanomaterials for Bio-Realistic Neuromorphic Computing

Brain-inspired computing hardware is emerging as an alternative to silicon complementary metal-oxide semiconductor chips in order to address the energy crisis of rapidly increasing digital data generation and processing. Conventional non-volatile memories can realize highly parallelized in-memory computing in neural networks, but they lack the adaptability and reconfigurability that are the key attributes of low-energy biological systems. To this end, neuromorphic devices based on low-dimensional nanomaterials embody bio-realistic tunable learning, coupled state variables, non-linear responses, and multi-terminal architectures of synapses.[1-5] In this talk, I will make the case for two-dimensional (2D) nanomaterials for neuromorphic hardware by drawing parallels between fundamental properties, form factors, and required functionalities in artificial synapses, neurons, and network architectures.[3,6,7] As a few examples, MoS2 memtransistors show bio-realistic synaptic learning in scalable crossbar arrays for higher-dimensional dynamic neural networks, multiple-agent reinforcement learning, and cerebellum-inspired anomaly detection.[7,8,9] Anomalous ferroelectricity in bilayer graphene moiré synaptic transistors achieves unprecedented adaptive learning.[10] Dual-gated, self-aligned Gaussian heterojunction transistors not only realize complex spiking behavior but also enable low-power hardware for machine learning algorithms in edge computing.[11,12] Bio-realistic spiking neurons can be printed on flexible substrates and demonstrate biocompatibility by direct communication with mouse cerebellum cells.[13] Refs: [1] Chem Rev. (2025), [2] Nature Electronics (2024), [3] Advanced Materials (2022), [4] Matter (2022), [5] Nature Nano. (2020), [6] Nature Nano. (2015), [7] Nature (2018), [8] ACS Nano (2026), [9] Nature Comm (2026), [10] Nature (2023), [11] Nature Comm. (2020), [12] Nature Electronics (2023), [13] Nature Nano. (2026)