Building Automated Scientists: From Physics Discovery to Atomic Fabrication

Abstract: The last note left by Richard Feynman stated “What I cannot create, I do not understand.” Building solid state quantum computers, creating nanorobots, and designing new classes of biological molecules and catalysts alike requires the capability to manipulate and assemble matter atom by atom, probe the resulting structures, and connecting them to macroscopic world. We are now at a pivotal moment where unprecedented investments in machine learning (ML) and artificial intelligence (AI) spark tremendous interest in automated discovery – from co-scientists to fully autonomous research systems. However, the pathways to building and benchmarking these systems are just emerging. 

We pose that the execution and assessment of daily research efforts hinges on multiobjective reward functions, which can be evaluated either during or at the conclusion of an experimental campaign. Correspondingly, the implementation of autonomous experimental workflows in automated laboratories necessitates the formulation of robust reward functions and their seamless integration across various domains. Here, I will present our latest advancements in the development of autonomous research systems based on electron and scanning probe microscopy, as well as for automated materials synthesis based on reward driven workflows and reward integration across domains. We identify several categories of reward functions that are discernible during the microscopy experiment, including imaging optimization, fundamental physical discoveries, and the elucidation of correlative structure-property relationships. The operationalization of these rewards function on autonomous microscopes is demonstrated, as well as strategies for human in the loop intervention. I will demonstrate how reward-based automated characterization can be used to scale up the throughput of fixed policy methods, enable the physics discovery across the combinatorial libraries, and learning correlative structure-property relationships. Finally, I will discuss the opportunities and strategies for direct atomic fabrication via electron beams, targeting desired structures and desired functionalities. Looking ahead, this work lays the foundation for decision making frameworks for the automated lab of the future, where human intuition and AI-driven autonomy work in synergy to drive materials discovery and atomic fabrication at an unprecedented scale. 

Bio: Sergei Kalinin is a Weston Fulton chair professor at the University of Tennessee, Knoxville. In 2022 – 2023, he has been a principal scientist at Amazon special projects (moon shot factory). Before then, he spent 20 years at Oak Ridge National Laboratory where he was corporate fellow and group leader at the Center for Nanophase Materials Sciences. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research focuses on the applications of machine learning and artificial intelligence methods in materials synthesis, discovery, and optimization, automated experiment and autonomous imaging and characterization workflows in scanning transmission electron microscopy and scanning probes for applications including physics discovery, atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy. When at ORNL, he led several major programs integrating the ML and physical sciences and instrumentation, including the Institute for Functional Imaging of Materials (IFIM 2014-2019), the first program in DOE integrating ML and physical sciences, and the microscopy effort in INTERSECT program that realized first ML-controlled scanning probe and electron microscopes. At UTK MSE, he participated in building one of the first efforts in the country on ML-driven materials exploration. At UTK, his team has now realized fully AI-controlled SPM and STEM systems and co-orchestration workflows between multiple characterization tools for scientific discovery. He has also taught multiple courses on the ML for materials science and microscopy including Bayesian optimization methods. Sergei has co-authored >650 publications, with a total citation of ~58,000 and an h-index of ~118. He is a fellow of NAI, Academia Europaea, AAAS, RSC, AAIA, MRS, APS, IoP, IEEE, Foresight Institute, and AVS; a recipient of the Adler Lectureship (APS 2025), Duncumb Award (MSA 2024), Medard Welch Award (AVS 2023), Orton Lectureship (ACerS 2023), Feynmann Prize of Foresight Institute (2022), Blavatnik Award for Physical Sciences (2018), RMS medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton medal of Microscopy Society of America (2010); 5 R&D100 Awards (2008, 2010, 2016, 2018, and 2023); and a number of other distinctions. As part of his professional services, he organized many professional conferences and workshops at MRS, APS and AVS; for 15 years organized workshop series on PFM, and served/s on multiple Editorial Boards including NPJ Comp. Mat., J. Appl. Phys, and Appl. Phys Lett.