Nano & AI: A Nobel Partnership

The scientific community must remain vigilant, promoting AI that not only does things well but does good things. Quantum computing devices: Quantum computing holds the promise of exponentially increasing computing power by utilizing quantum bits (qubits) that exist in multiple states simultaneously. Nanotechnology is the enabler of creation of quantum materials and devices that enable stable and scalable qubits. (14−17) ACS Nano is at the forefront of research into quantum materials that exhibit exotic quantum properties. These materials could revolutionize quantum computing by enabling more stable and scalable qubits, leading to powerful and reliable quantum computers for AI applications. Nanofabrication techniques enable precise control at the atomic level, essential for constructing qubits with high coherence times. Advances in two-dimensional materials and topological insulators are also paving the way for the quantum computing devices. Neuromorphic computing devices: Neuromorphic computing aims to mimic the neural architecture of the human brain to achieve efficient computations. Developing artificial neurons based on chemical or electric devices requires nanoscale fabrication to achieve high speeds and low power consumption. (18) Recent advances include memristive devices that emulate synaptic functions, enabling hardware implementations of neural networks. (19−21) 3D architectures: Moving beyond the traditional 2D chip design, 3D architectures utilizing nanomaterials allow for higher transistor density and shorter interconnects, which in turn boosts computing performance and efficiency. (22,23) Nanosensors for data acquisition: AI thrives on data. Nanotechnology enables the development of highly sensitive and selective sensors that can gather vast amounts of data from the environment and importantly, directly from humans. This aspect comprises the research areas of wearable nanosensors, implantable nanosensors, nanosensors for brain–computer interfaces, and so on. (24) These sensor arrays generate rich data sets essential for training and improving AI algorithms, particularly in personalized medicine and human–machine interface. Nano and AI are increasingly intertwined, forming a Nobel partnership that holds immense promise for the future. Nanomaterials discovery: Combining advances in AI with robotics can revolutionize the discovery of new nanomaterials through the rise of automated laboratories. This approach relies on the integration of tools such as high-throughput virtual screening, automated synthesis planning, and machine-learning algorithms that are able to direct experiments and interpret results on-the-fly to design new procedures. Self-driving laboratories comprise intelligent robotic laboratory assistants that dramatically speed up the rate of lab-based discovery via rapid exploration of chemical space in a closed-loop format. (25,26) Their utility for the discovery and optimization of nanomaterials using both experimental approaches and simulations (27) is enormous. Nano characterization: AI enhances the accuracy of identifying nanoscale phenomena. Deep-learning models can be trained to support many analyses, including high-precision atom segmentation, localization, denoising, and super-resolving of atomic-resolution images recorded by TEM (28−30) identifying chemical features and decomposing their oxidation states using electron energy loss, X-ray absorption, and Raman spectroscopy, (31−35) inpainting the missing wedge in electron tomography, breaking the 0.7 Å 3D imaging barrier and enabling low-dose imaging and quantitative analysis, (36−40) and phase identification at the nano and atomic scales. (41−43) Structure–property relationships: Predicting the chemical and physical properties of a molecule from only its structure has long been an inaccessible dream for many chemists. In future years, it may become reachable, even in the case of complex nanomaterials, through the use of advanced AI models that have already shown their ability to efficiently learn correlations between variables. (44) Chemical sensing and disease screening: AI enables the automatic identification of targets with high precision. Nanosensors combined with AI algorithms improve the detection of biomarkers for diseases, environmental pollutants, and chemical threats. (45) Figure 1. Prof. Yang Chai (left) and Prof Maria Lukatskaya (right) were appointed as Associate Editors of ACS Nano since September 2024. Photograph courtesy of Yang Chai and Maria Lukatskaya. This article references 45 other publications. This article has not yet been cited by other publications.