Keynote Speakers
(in alphabetical order)
 | Rong Chen School of Mechanical Science and Engineering Rong Chen is a full professor at Huazhong University of Science and Technology with the School of Mechanical Science and Engineering, by courtesy of China-EU Institute for clean and renewable energy of HUST, and college of future technologies. She received her M.Sc. and Ph.D. degrees from Stanford, B.S. from University of Science and Technology of China. She was a senior research scientist at Intel Labs before she joined HUST. Her research focuses on atomic level manufacturing, by understanding surface science, and applying to a range of problems in semiconductor manufacturing, nanotechnology, and sustainable energy. |
| Atomic layer infiltration Engineered Quantum Dot Architectures: Towards High-Fidelity Wearable Pulse Wave Sensing | |
Abstract: Atomic layer deposition–enabled infiltration provides a powerful route for atomic-scale control of quantum dot (QD) materials, yet its impact on device-level functionality remains underexplored. Here, we present an atomic layer infiltration (ALI) strategy to engineer QD architectures for high-fidelity wearable pulse wave sensing. Conformal infiltration of composite oxides stabilizes QD surfaces and suppresses non-radiative recombination, forming a well-defined bridge–trap architecture that reduces dark current while enhancing radiative pathways via electron localization. At the interface level, atomically inserting barrier layers establish a barrier–channel architecture that regulates carrier transport across heterogeneous environments, minimizing RC delay and improving temporal response. Furthermore, selective infiltration on self-assembled QD arrays yields stretchable, air-stable luminescent films that retain optoelectronic performance under mechanical deformation. These atomic-scale design principles are broadly applicable to diverse material systems, including perovskite and organic semiconductors. As a result, the devices enable real-time pulse wave sensing with high signal fidelity, supporting accurate heart rate and blood oxygen monitoring with excellent operational stability. This work highlights atomic layer infiltration as a scalable manufacturing paradigm bridging atomic-level interface control and wearable optoelectronics. | |
 | Xin Chen Department of Materials Intelligence Dr. Xin Chen earned his B.E in materials science from University of Science and Technology of China and Ph.D. in chemistry from Stanford University. He was a professor at Boston University before he joined Suzhou Laboratory, where he works as a fellow scientist and the deputy director of Department of Materials Intelligence. Since 2024, he has been leading a major project of SciTech Innovation 2030, and serve as a guest professor at University of Science and Technology of China, Department of Artificial Intelligence for Science. His recent research interest focuses on using generative AI to assist materials design, including using spectroscopy embedded CNN for property prediction and developing multi-model langrage models specialized in chemistry and materials science as potential co-scientists. |
| A Multi-Modal Large Language Mode for Materials Science and Its Applications in New Materials Discovery | |
Abstract: Rapid developments of AI tools are expected to offer unprecedented opportunities for materials science. To integrate specialized language and domain knowledge including various forms of molecular presentations and spectroscopic methods, we first developed a 13B LLM trained on 34B tokens from chemical literature, textbooks, and instructions. The resulting model, ChemDFM, can store, understand, and reason over chemical knowledge while still possessing generic language comprehension capabilities. We further developed a multi-modal LLM for chemistry and materials science: ChemDFM-X. Diverse multimodal data includes SMILES, molecular GNN, mass spectroscopy and IR spectroscopy, etc, generating a large domain-specific training corpora containing 7.6M data. The resulting ChemDFM-X demonstrates the great potential in inter-modal knowledge comprehension. The LLMs have generated more 250K downloads from Huggingface. In the last, I will present a few examples of using LLM as scientific research agents or co-scientists for new materials design and preparation, including several chiroptical membranes and self assembled monolayers for perovskite photovoltaic cells. | |
 | Yuanliu Chen School of Mechanical Engineering Prof. Yuan-Liu Chen is the Qiushi distinguished professor of Zhejiang University. He is the winner of National Science Fund for Outstanding Young Scholars. He is serving as the vice dean of Mechanical Engineering of Zhejiang University. His research interests lie primarily in the field of precision engineering, in-process smart measurement and ultraprecision cutting. He has worked out a lot of manufacturing/measurement integrated systems as well as a couple of sensor technologies including force controlled fast tool servo for precision manufacturing, on-machine and in-process surface measurement technologies. He has published over 100 scientific papers over 40 patents. He is the Associate Memeber of the International Academy for Production Engineering (CIRP). Prof. Yuanliu Chen is awarded by Best Paper Award (Japan Society for Precision Engineering, JSPE), Young Researcher Award (Japan Society of Mechanical Engineering), Distinguished Young Scholar of China Frontiers of Engineering (Chinese Academy of Engineering), the first-class prize of Chinese Machinery Industry Science and Technology (2023). |
| Smart Thermal Sensing and Regulation Tool for Ultra-precision Machining | |
Abstract: The ability to precisely thermal monitor and regulate at the nanoscale cutting zone is a critical challenge in ultra-precision machining, fundamentally limited by the spatial inaccessibility of conventional sensors and heaters. To overcome this barrier, this research presents a smart diamond tool engineered to function intrinsically as both a temperature sensor and an active thermal regulator. This paradigm enables direct in-process monitoring and controlling of the thermal dynamics at the tool-workpiece interface for the first time. Leveraging this capability, we demonstrate two significant advancements: on-machine identification of surface topography and micro-defects based on real-time monitoring of cutting temperature, as well as high-efficiency, ductile-mode machining of brittle materials through precisely controlled thermal softening. These approaches established a new pathway for active thermal management in advanced manufacturing processes. | |
 | Hui Deng Department of Mechanical and Energy Engineering Dr. Deng received the bachelor’s degree from Huazhong University of Science and Technology (HUST) in 2010. Then he studied in Osaka University, Japan and received the Ph.D. degree in precision science and technology in 2016. From 2016 to 2017, he worked in Singapore Institute of Manufacturing Technology (SIMTech) as a research scientist. Since 2017, Dr. Deng joined Southern University of Science and Technology (SUSTech) and established the Advanced Plasma Manufacturing Lab. His research interest is focusing on atomic and close-to-atomic scale manufacturing based on physicochemical approaches. |
| Recent progress on surface reconstruction at the atomic scale | |
Abstract: In this presentation, two atomic scale surface reconstruction methods will be introduced: plasma-induced surface annealing and tribochemistry induced surface patterning. For plasma annealing, we find that three reconstruction modes, namely, 2D-island, step-flow, and step-bunching, can be identified with the increase in the input power; only the step-flow mode can result in the formation of an atomically smooth semiconductor surface (Sa = 0.098 nm). By utilizing the atomic-scale material removal capability during the friction process between SiO2 and sapphire, surface atoms of sapphire are selectively removed, enabling the fabrication of nearly damage-free nanostructures with hardness comparable to that of bulk sapphire. The mechanism and influence of processing parameters on the fabrication of sapphire nanostructures were investigated. As typical abrasion-free and atomic-scale surface processing methods, plasma and tribochemistry-based surface reconstruction are expected to enrich the theoretical and technological knowledge on atomic-scale manufacturing. | |
 | Lingbao Boby Kong College of Future Information Technology Prof Lingbao Kong got his BEng and MEng degrees from Harbin Institute of Technology, and PhD degree from The Hong Kong Polytechnic University. Currently he is a Full Professor and Director of Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing of Fudan University. Prof. Kong has been engaged in ultra-precision optical manufacturing and measurement for nearly twenty years. His research interests include ultra-precision machining and intelligent vision measurement, optical freeform machining and measurement, design and generation of functional structures, multi-spectrum and vision inspection, etc. He has led/participated in a series of national research projects and industrial collaborative projects. He has published over 260 referred research papers, got over 30 granted patents, authored 1 book and a series of book chapters. As an active researcher, Prof. Kong ranked top 2% scientists (2025), has received Gold Award in World Photonics Conference Invention Exhibition 2025, Outstanding Alumni Award of Department of ISE and Faculty Awards for Outstanding Performance/Achievement from The Hong Kong Polytechnic University. He also received a series of national and international awards including Technology Progress Award from Ministry of Education of China, Invited Young Researcher from ASPEN, Joseph Whitworth Prize and A M Strickland Prize from IME of UK, Geneva Invention Awards, etc. He is the chairman of Advanced Optical Manufacturing Committee (Young) in Chinese Society for Optical Engineering, the editorial board members of Chinse Journal of Mechanical Engineering and International Journal of Extreme Manufacturing, etc. |
| Towards Nano and Sub-nano Scale Optical Measurement Technology | |
Abstract: With the advances of atomic-scale manufacturing technologies, conventional measurement methods such as SEM, TEM, AFM, etc. encounter limitations of high costs, low throughput, and difficult process integration. Optical measurement approaches have potentials and superiorities to address the above challenges. This talk will present the recent research work undertaken by the speaker’s group, which underlines the theoretical models and prototyping systems aiming to detect nano-scale and sub-nano scale features based on optical approaches. An integrated dark-field heterodyne interferometric scattering detection system is established to verify the weak-signal detectability, nanoscale quantification capability, and large-area inspection performance. Detection of 5 nm-scale scatterers under 532 nm @ 100 mW laser illumination is realized experimentally. Sub-nano scale measuring method is proposed by integrating weak measurement with the Goos–Hänchen effect. The pseudo-Brewster angle over macroscopic sample is found via the photon spin Hall effect under weak measurement condition. The angstrom level of lateral resolution is achieved by beam shaping and controlled nanoscale scanning using electro-optic beam deflection. Numerical simulations and experimental validation on exfoliated and CVD-grown MoS₂ flakes confirm the quantitative accuracy, reproducibility, and scalability of the proposed method, demonstrating its strong potentials as a low-cost, non-destructive, and industrially viable method for atomic scale measurement. | |
 | Zhirong Liao Faculty of Engineering Prof. Zhirong Liao finished his PhD study at Harbin Institute of Technology and joined the Rolls-Royce UTC in Manufacturing and On-Wing Technology, University of Nottingham, as a research fellow in 2016. He was awarded a Nottingham Research Fellowship and started his independent academic career at the University of Nottingham in 2019, and was promoted to Associate Professor in 2022, and Professor (Chair of Advanced Manufacturing) in 2025. His research area mainly focuses on conventional and nonconventional manufacturing technologies with conscious of materials science. His expertise covers machining, laser materials processing, micromechanics, machine tools, sensing, and precision engineering. He has been awarded more than £8 million research grants as PI and CoI. His research has been applied to aerospace, automotive and medical engineering sectors. He is the winner of Rising Star of the University of Nottingham Knowledge Exchange and Impact Awards (2023). He is an Associate Member of the International Academy for Production Engineering (CIRP), Chairman of CIRP UK, Member of the Institution of Mechanical Engineers (IMechE). Prof. Zhirong Liao is also the Editor-in-Chief of the Journal of Materials Processing Technology and an Associate Editor of the International Journal of Machine Tools and Manufacture, Editorial board member of Chinese Journal of Mechanical Engineering, which are top journals in the manufacturing field. |
| Inverse problem-based laser modulation for advanced materials processing | |
Abstract: In laser materials processing, e.g. welding, additive manufacturing, or even laser assisted machining, laser beam configuration is of importance for precise control of the process quality. In this context, modulating the spatial thermal field distribution has become a critical challenge. One approach is to control the laser-materials interaction through predefined laser beam scanning strategy, which does not only avoid the excessive heat input of conventional methods but also enables control of the temperature distribution to manage the heating width and penetration depth, while prevent overheating defects. However, the common choice of laser beam paths is typically based on user experience which involves many modelling and experimental iterations until a close to satisfactory, but not optimal limited its application of wide laser modulation. In recent years, University of Nottingham has developed an “Inverse Problem” based method to optimize the laser scanning strategy parameter. Our method yields variable beam scanning speeds and optimized beam paths for achieving a desired heat placement (uniform or target pattern) and is suitable for different circumstances and scanning strategies dependent on the materials processing configuration. This method has been proved in the use of laser welding, additive manufacturing, and laser assisted machining processes. | |
 | Kui Liu College of Mechanical and Electrical Engineering Dr LIU KUI is distinguished professor at the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics (NUAA), China, focusing on ultra-precision machining (UPM), atomic and close-to-atomic scale manufacturing (ACSM) and intelligent manufacturing. Before joining NUAA in 2025, he was Principal Scientist at Singapore Institute of Manufacturing Technology. He obtained his BEng and MEng degrees from NUAA in 1989 and 1996, and PhD degree from National University of Singapore in 2002. As a PI and Co-PI, he has secured 67 competitive and industrial funds in Singapore with the total grants around SGD47.0M. He has authored 2 monographs and 6 book chapters, published 210 papers in prestigious journals and conferences, of which 6 papers have been selected as Highlights the Best of A*STAR, filed 7 patents and 27 technical disclosures. He is a board member of ASPEN and ICAT, an editorial board member of SCI journals IJEM, NMME, Machines and DAE. He also won few outstanding awards including China Nation-level Professorship, ISNM Contribution Award, NMME Excellent Editor, A*STAR Aerospace Program Achievement Award, SIMTech Best Research Achievement and Best Industrial Achievement Awards. |
| Ultra-precision machining towards atomic and close-to-atomic scale manufacturing | |
Abstract: Atomic and close-to-atomic scale manufacturing (ACSM), as the next generation of manufacturing technology, where energy is used to directly impact atoms so as to be removed, migrated, and added, has being attracted more and more attentions from both academy and industry. ACSM will be employed to build atomic-scale features/surfaces for the expected functionalities and performance, of which it will be the leading technology development trend in manufacturing for future. While ultra-precision machining (UPM) is a being established key technology for advanced manufacturing, which is capable to produce mechanical, optical and optoelectronics components achieving a surface roughness with few nanometer and form accuracy in sub-micron range. But it is not easy and very challenge to make UPM into ACSM. In this talk, UPM towards ACSM particularly on material removal will be addressed and discussed in different aspects. | |
 | Zhu Liu Research Centre for Laser Extreme Manufacturing Zhu Liu received her PhD degree in Materials Science from Liverpool University, UK. She served as a tenured professor at the University of Manchester, UK, before joining the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), in 2023. At NIMTE, she has established a research team focusing on the laser synthesis of new energy materials and has led various R&D projects at the national, Zhejiang provincial, and Ningbo municipal levels. She has developed a laser-based solid-phase synthesis method for atomically dispersed metallic materials, with related technologies applied in areas such as hydrogen production via water electrolysis, carbon dioxide hydrogenation, and the green manufacturing of flexible perovskite solar cells. She has published 190 SCI papers in international journals (including Light: Science and Applications, Nature Communications, Advanced Energy Materials, and Advanced Functional Materials), with over 8,100 citations and an h-index of 49. |
| Laser solid-phase synthesis of atomically dispersed metallic materials | |
Abstract: Atomically Dispersed Metallic Materials (ADMMs), featuring isolated metal atoms on a solid support, have been widely used in energy conversion and storage devices. Currently, the main challenge in their mass production lies in balancing the trade-off between atomic-level precision and efficient, reliable, low-cost manufacturing. This necessitates the exploration of new mechanisms and technologies for the efficient construction of ADMMs. Here, we report a laser solid-phase synthesis technique based on laser-induced carrier defect anchoring of atomic-scale building blocks, enabling precise control over the composition and structure of ADMMs. By regulating the type, ratio, distribution, and bonding state of the constituent elements, we have achieved the fabrication of single-atom, diatomic, atomic-cluster, and high-entropy atomically dispersed materials through laser irradiation and integrated structural design, enabling efficient production. This approach shows great potential in catalysis. The strong interaction between the laser and the precursors/supports introduces new mechanisms, offering fresh insights for a laser-based, efficient construction technology system that provides a low-cost, reliable, and highly versatile solution for atomically precise mass manufacturing. This work advances the industrialization of laser technology in the preparation of atomically dispersed metallic materials. | |
 | Xichun Luo Department of Design, Manufacturing and Engineering Management Xichun Luo is a Professor in Ultra Precision Manufacturing, Technical Director of Centre for Precision Manufacturing at the University of Strathclyde, UK. His research interests include ultra-precision machining, micro, nano, atomic and close-to-atomic scale manufacturing. |
| Manufacturing III and Atomic and Close-to-Atomic Scale Manufacturing | |
Abstract: This talk will first highlight three manufacturing paradigms, classified according to the scale of precision, functional features, and manufacturing manipulation. It will then introduce key Manufacturing III technologies, including quantum technologies, artificial intelligence, digital technologies, control methods, and atomic and close-to-atomic scale manufacturing (ACSM). | |
 | Rong Su Department of Advanced Optical and Microelectronic Equipment Rong Su is a Professor at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, where he also serves as Vice Director of the Department of Advanced Optoelectronic Equipment. After earning his Ph.D. in Optical Metrology from the KTH Royal Institute of Technology, he spent six years at the National Physical Laboratory and the University of Nottingham in the UK before returning to Shanghai. Su has a long-standing commitment to the development of ultra-precision optical instruments and technologies, particularly for surface metrology. His expertise includes the design and development of interferometers, interference microscopes, and scattering instruments, as well as physical modeling, system calibration, and uncertainty analysis. He has authored over 60 peer-reviewed publications and holds more than 10 invention patents. In 2021, he was awarded a fellowship from the Chinese Academy of Sciences. Active in the global metrology community, Su serves on the ISO TC213/WG16 committee and is a member of the editorial boards for three scientific journals, including Nanomanufacturing and Metrology and Light: Advanced Manufacturing. |
| Development of 3D optical profilers with picometer-level measurement accuracy | |
Abstract: In the exposure system of high numerical aperture EUV lithography system, the maximum diameter of the mirror can reach 1 meter, and the length of X-ray long strip mirrors used in synchrotron radiation facilities can be up to 1.5 meters. Such optical components impose extremely stringent manufacturing precision requirements. It is necessary not only to control the geometric surface shape error, but also to suppress the mid-frequency errors and high-frequency surface errors down to the sub-nanometer level. Therefore, picometer-level accuracy of surface measurement is demanded. It is essential to explore approaches based on fundamental physical modeling and optical design to drive the innovative development of 3D optical profilers. | |
 | Jianfeng Xu School of Mechanical Science and Engineering Prof. XU Jianfeng is the Vice Dean of the School of Mechanical Science and Engineering at Huazhong University of Science and Technology, and a recipient of the National Science Fund for Distinguished Young Scholars. His primary research interests lie in ultra-precision intelligent manufacturing. He has led numerous research projects, including those funded by the National Natural Science Foundation of China (NSFC), the National Key R&D Program of the Ministry of Science and Technology, and the High-Quality Development Special Project of the Ministry of Industry and Information Technology (MIIT). In addition, he has undertaken the Intelligent Manufacturing Special Project of the MIIT, the Major International Cooperation Project of the Ministry of Science and Technology, the Joint Key Project of the NSFC, and the 13th Five-Year Plan Equipment Pre-Research Project of the Equipment Development Department, among others. He has published more than 100 papers in leading domestic and international journals and conferences in the field of manufacturing and holds over 40 authorized patents related to precision intelligent manufacturing technologies and equipment. |
| Laser-based Ultra-precision Manufacturing: From Cutting to Ultrafast Micro/Nano Fabrication | |
Abstract: As a core technology in advanced manufacturing, lasers have found extensive industrial applications owing to their high energy density, non-contact nature, and superior controllability. Our team has conducted systematic investigations into ultra-precision machining of brittle materials, focusing on laser-assisted machining, laser polishing, and ultrafast laser processing. In laser-assisted machining, we elucidated the material removal mechanisms under laser irradiation and developed a series of in-situ laser-assisted systems, achieving high-quality, low-damage machining of various brittle materials. For laser polishing, we employed short-pulse lasers, established a solid–liquid transition flow model describing melt pool cooling, and developed a multi-energy-field-coupled ultra-precision manufacturing platform capable of smoothing tool marks and repairing subsurface damage. To meet the demands of advanced packaging for high-performance AI chips, we further investigated laser processing of transparent substrates, revealed the crack damage mechanisms inherent to ultrafast laser processing, and developed an ultra-high-speed, crack-free precision processing system, enabling efficient fabrication of micro-holes in packaging substrates. Through these advancements, our team has explored laser applications across three dimensions—efficiency enhancement in ultra-precision cutting, ultra-smooth surface modification, and precision micro-machining—driving modern precision manufacturing toward higher accuracy, greater efficiency, and broader material applicability. | |
 | Shuming Yang School of Mechanical Engineering Shuming Yang, ISNM Fellow, VEBLEO Fellow, the current president of ASPEN, a full professor of Xi’an Jiaotong University (XJTU), China. His main research interests include nanofabrication and measurement, optical measurement and instrumentation, intelligent manufacturing etc. He has held more than 20 research projects including National Science Fund for Distinguished Young Scholars, National Key R&D Program of China, National Science and Technology Major Projects etc. He has published over 300 papers and 3 books, owned over 100 patents of PCT, UK, European and China, achieved more than 10 technical awards. He is an editor of JMS, NMME, IJPEM- GT, PE, IJRAT, etc. He delivered plenary/keynote/invited talks in academic conferences for more than 100 times. |
| Nano-probe based metrology toward for ACSM | |
Abstract: Nano-probe based method is a direct and traceable way to complete measurement. We have designed and developed nano probes with special structures to realize measurement at nano-scale even atomic-scale. The key challenge is the weak signal and we have to enhance it. The signal-to-noise ratio was much improved by optimizing the probe structure and adopting surface plasmons to increase the focusing intensity of the tip optical field. We have achieved the measurement resolution around 5 nm by nano-probe based metrology so far. In the future, it has potential to obtain a higher resolution less than 1 nm. | |
 | Ruzhi Zhang Division of Information Materials Dr. Zhang serves as the Deputy Chief Scientist of Suzhou Laboratory, overseeing key semiconductor technologies and materials. He is also the Chief Scientist of Electronic Chemicals Industry Center (ECIC). He received his Ph.D. in Chemistry from the University of Cincinnati in 1999. He completed his postdoctoral fellowship in the Department of Chemistry at the University of Wisconsin-Madison from 1999 to 2000.Beginning in 2000, Dr. Zhang has been engaged in the discovery of materials for semiconductors and integrated circuits. He has led R&D teams at several leading international companies, focusing on the development and commercialization of advanced and cutting-edge semiconductor materials. Dr. Zhang has authored numerous papers in international journals and holds numerous granted patents in the United States, China, and other countries. He has delivered presentations at many international conferences. |
| AI-enabled Discovery of New Lithographic Materials | |
Abstract: The relentless drive toward smaller semiconductor nodes demands advanced lithographic materials with exceptional resolution, sensitivity, line-edge roughness (LER), and defect control. To overcome the RLS trade-off dilemma, we believe that co-optimization of both the exposure and development spaces is essential. Therefore, we have carried out a methodological investigation into this co-optimization. Negative tone development (NTD) processes offer unique advantages for specific patterning applications and serve as an ideal case study for our approach. We systematically explore the NTD developer formulation dimension alongside the photoresist-formulation-related exposure space, using our in-house AI4S-MDS (AI for Science – Materials Discovery/Design System) framework and machine learning (ML) tools. The presentation highlights recent findings on high-performance NTD developers, identified through high-throughput experimentation (HTE) and AI4S-MDS. | |
 | Haitao Zhao Department of Electrical and Electronic Engineering Prof. Haitao Zhao is Professor and Director of the Research Centre for Materials Intelligent Manufacturing in the Department of Electrical and Electronic Engineering and a Direction Leader of the State Key Laboratory of Ultra-Precision Machining Technology at The Hong Kong Polytechnic University. His research focuses on Materials Intelligent Manufacturing through the convergence of robotics, artificial intelligence, and data-driven methodologies, with particular emphasis on advancing the State Key Laboratory’s directions in intelligent materials manufacturing, robotic experimentation, and autonomous laboratory systems. He works on intelligent frameworks that connect rational design, controllable synthesis, and inverse design for advanced materials research and manufacturing. His research aims to accelerate the creation of energy and semiconductor materials by integrating robotic experimentation, domain-specific modelling, and structured materials knowledge into closed-loop research workflows. He has published over 100 peer-reviewed papers and has contributed to influential work in robotic synthesis, intelligent automated laboratories, and data-driven materials innovation. He also serves the research community through editorial and professional roles in artificial intelligence, materials science, and smart manufacturing. |
| OSCAR New Initiatives: AI+Robotic Lab Collaborative Research and Development of Automation for the Full-Process Fabrication of Perovskite Solar Cells | |
Abstract: The OSCAR AI+Robotic Lab initiative is a collaborative R&D platform that targets end-to-end automation of perovskite solar cell (PSC) fabrication. Led by Prof. Haitao Zhao, the work centres on converging AI models with robotic hardware to evolve materials manufacturing from manual experimentation toward intelligent, autonomous operation. This initiative contributes to the State Key Laboratory of Ultra-Precision Machining Technology by advancing intelligent manufacturing and high-precision automated laboratory systems for next-generation materials research. The core technical contribution is the Agentic Automation System framework, comprising of eleven interconnected robotic modules (B01 through B11) orchestrated by a domain-specific Recipe Language Model (RLM). The RLM converts experimental parameters (Formulas and Parameters) into machine-readable, robot-executable recipes covering the full manufacturing sequence, from solution preparation and spin-coating to high-vacuum deposition and in-situ characterisation. A closed-loop paradigm integrating large language models, digital twins, and robotic agents drives iterative optimisation across data mining, rational design, controllable synthesis, and inverse manufacturing. The project is also advancing two IEEE standards (P3466 and P3467) to formalise data-driven laboratory intelligence and the digital manufacturing workflow for perovskite modules, establishing a replicable paradigm for autonomous materials production. | |