EXTENDED ABSTRACT: The integration of Artiffcial Intelligence (AI) with automated intelligent laboratories offers new possibilities for the design and development of advanced materials. Automation and intelligence in laboratory equipment are essential for enhancing research efffciency and precision. Traditional laboratory operations, are not only time-consuming and labor-intensive but also prone to human error. Additionally, experimental process can be difffcult to trace due to the variability in recording and storage practices among different researchers, ultimately affecting the accuracy and reproducibility of results. With the advancement of technology, automated laboratory systems are becoming increasingly prevalent. However, most current market solutions are focused on standardized hardware and software conffgurations, lacking the level of customization and adaptability required to address the specific needs of research institutions and enterprises. This limitation makes it challenging to meet the demands in various laboratory scenarios. Consequently, both academy and industry are finding laboratory automation solutions that integrate cutting-edge AI technologies while accommodating customized requirements. This paper uses nanomaterials research as a case study to illustrate how AI and automated intelligent laboratories are driving innovation in material design and development. Capitalizing on its deep understanding of automation needs in chemical laboratories. Chemical.AI recently collaborates with a renowned foreign university and implemented customized integration of existing hardware infrastructure. Through these customized upgrades, Chemical.AI has established the “Nanomaterial Analysis and Testing” platform, achieving 24-hour unattended Raman spectroscopy testing. The platform significantly enhances laboratory automation level through features such as automated material loading and unloading, visual recognition, XYZ three-dimensional positioning, and online Raman detection. Supported by this platform, the throughput of silicon waferloaded samples has increased substantially, with over 1,000 samples tested daily—three times the efffciency of traditional manual testing. Furthermore, the system integrates intelligent functions such as online data storage, baseline noise removal, automatic peak identiffcation and deffnition, greatly improving the accuracy and reproducibility of experimental data. Users can also remotely monitor and control the system, providing real-time updates on experimental progress, further increasing the ffexibility and convenience of the research process. With the integration of AI technologies, laboratory automation is no longer limited to hardware upgrades but is now deeply integrated with intelligent data analysis and experiment workffows, signiffcantly enhancing research efffciency and precision. Through its collaboration with academic institutions, Chemical. AI has successfully developed a highly efffcient research platform for nanomaterials, making a new milestone in scientiffc innovation in the ffeld of advanced materials.

Keywords: AI; Automation; Advanced material development; Nanomaterials; Intelligent laboratory;