Scripted Automation for Efficient Control and Autonomous Experimentation with AFM
Expedite your workflows with scripted instrument control, ML integration, and agentic AI
Wednesday, July 22 | 8AM PDT | 11AM EDT | 5PM CEST
Automated AFM Experimentation and Materials Discovery
In this webinar, presenters will demonstrate the use of NanoScope API for safe and flexible control of Bruker Dimension series AFMs. They will also show how integrating this API with machine learning and agentic AI automates the SPM for materials discovery while reducing the coding burden on users.
Join us for this webinar to see:
- The new nanoscope_api interface that provides access to control Dimension AFMs
- Examples of scripted instrument control for lithography, parameter control, and more
- Integrations and implementations for added utility and usability, from external instrumentation to machine learning and agentic AI.
Wednesday, July 22 | 8AM PDT | 11AM EDT | 5PM CEST
Scripted Automation for Efficient Control and Autonomous Experimentation with AFM
The sensitivity, resolution and multi-modal capabilities of the atomic force microscope make it attractive in development and characterization of devices and materials. NanoScope API, a new application programming interface (API), has been developed for safe and efficient measurement of large samples and/or sets of samples with Dimension AFM systems.
We consider the use of this API to control system parameters, conduct nanolithography, check system status, collect data, change modes, and move the stage to access different samples or locations on a 200 mm sample. By integrating this new API with machine learning–driven experimental control we further demonstrate how the SPM can be automated for materials discovery. Finally, we discuss how agentic AI can be harnessed to access this API and reduce or eliminate the burden of coding for the user.
- Introduction to nanoscope_api: a new interface that provides safe and flexible access to control of Bruker Dimension series AFMs
- Examples of scripted instrument control for applications ranging from lithography to control of key instrument parameters during scanning
- Integration of scripted AFM with external instrumentation to expand system capabilities
- Automated experimentation for efficient exploration of combinatorial libraries for materials discovery
- Agentic AI for easy script development
Date: July 22, 2026
Time: 8AM PDT | 11AM EDT | 5PM CEST
Location: Online
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Speakers
Richard (Yu) Liu, Ph.D.
Postdoctoral Researcher, AI-Enabled SPM, University of Tennessee KnoxvilleRichard (Yu) Liu is a Postdoctoral Researcher at the University of Tennessee Knoxville, working on ML-enabled autonomous experiments for materials discovery. He received his Ph.D. in Physics from Harvard University and has extensive experience in scanning probe microscopy, automated instrument control, and nanoscale studies of quantum and functional materials. His recent work focuses on building automated SPM workflows for reward-driven imaging, spectroscopy, high-throughput combinatorial-library characterization, and multi-objective active learning for autonomous materials discovery.
Bede Pittenger, Ph.D.
Sr. Staff Development Scientist, AFM Applications, Bruker Nano SurfacesBede Pittenger is a Senior Staff Development Scientist in Bruker's AFM Unit. He received his PhD in Physics from the University of Washington (Seattle, WA) and has more than 25 years experience on SPM, building systems, developing techniques, and studying properties of a wide variety of materials at the nanoscale.
Peter De Wolf, Ph.D., Director of Technology & Application Development
Peter De Wolf is director for AFM technology & application development at Bruker Nano Surfaces, covering all applications related to Scanning Probe Microscopy (SPM). He obtained his PhD from IMEC, Belgium on the development of new SPM methods for 2D carrier profiling in semiconductors and has more than 25 years of experience on SPM. He is the author and co-author of over 30 publications related to electrical characterization using SPM. He also owns several SPM patents, and developed several new SPM modes for electrical characterization.
