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Bayesian Co-Navigation of a Computational Physical Model and AFM Experiment to Autonomously Survey a Combinatorial Materials Library
Authors:
Boris N. Slautin,
Kamyar Barakati,
Yu Liu,
Reece Emery,
Philip Rack,
Sergei V. Kalinin
Abstract:
Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the first fully automated experimental realization of Bayesian co-navigation - a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical…
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Building autonomous experiment workflows requires transcending beyond the data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the first fully automated experimental realization of Bayesian co-navigation - a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical model. Using an automated AFM platform coupled to a kinetic Monte Carlo (kMC) model of thin-film growth, the system infers a set of effective bond energies for the (CrTaWV)x-Mo(1-x) pseudo-binary combinatorial library, progressively adjusting the kMC parameters to decrease the epistemic disparity between simulation and experiment. This real-time theoretical refinement enables the kMC model to capture the behavior of the specific materials system and reveals the mechanistic role of hetero-bonding in governing surface diffusion. Together, these results establish co-navigation as a general strategy for tightly integrating physical models with autonomous experimental platforms to produce interpretable and continually self-correcting theoretical modelling of complex materials systems.
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Submitted 8 December, 2025;
originally announced December 2025.
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Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM
Authors:
Boris N. Slautin,
Yongtao Liu,
Kamyar Barakati,
Yu Liu,
Reece Emery,
Seungbum Hong,
Astita Dubey,
Vladimir V. Shvartsman,
Doru C. Lupascu,
Sheryl L. Sanchez,
Mahshid Ahmadi,
Yunseok Kim,
Evgheni Strelcov,
Keith A. Brown,
Philip D. Rack,
Sergei V. Kalinin
Abstract:
For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and…
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For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization.
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Submitted 11 April, 2025; v1 submitted 5 January, 2025;
originally announced January 2025.
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Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy
Authors:
Yu Liu,
Roger Proksch,
Jason Bemis,
Utkarsh Pratiush,
Astita Dubey,
Mahshid Ahmadi,
Reece Emery,
Philip D. Rack,
Yu-Chen Liu,
Jan-Chi Yang,
Sergei V. Kalinin
Abstract:
Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use,…
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Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM.
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Submitted 25 December, 2024; v1 submitted 7 August, 2024;
originally announced August 2024.
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Invariant Discovery of Features Across Multiple Length Scales: Applications in Microscopy and Autonomous Materials Characterization
Authors:
Aditya Raghavan,
Utkarsh Pratiush,
Mani Valleti,
Richard Liu,
Reece Emery,
Hiroshi Funakubo,
Yongtao Liu,
Philip Rack,
Sergei Kalinin
Abstract:
Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting a…
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Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex datasets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structure. Here we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure-property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence, scale-invariant transformation fronts, etc.
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Submitted 31 July, 2024;
originally announced August 2024.
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Integration of Scanning Probe Microscope with High-Performance Computing: fixed-policy and reward-driven workflows implementation
Authors:
Yu Liu,
Utkarsh Pratiush,
Jason Bemis,
Roger Proksch,
Reece Emery,
Philip D. Rack,
Yu-Chen Liu,
Jan-Chi Yang,
Stanislav Udovenko,
Susan Trolier-McKinstry,
Sergei V. Kalinin
Abstract:
The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Py…
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The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements towards operationalization of automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer (HPC), which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.
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Submitted 20 May, 2024;
originally announced May 2024.
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Non-polar (11-20) InGaN quantum dots with short exciton lifetimes grown by metal-organic vapor phase epitaxy
Authors:
Tongtong Zhu,
Fabrice Oehler,
Benjamin P. L. Reid,
Robert M. Emery,
Robert A. Taylor,
Menno J. Kappers,
Rachel A. Oliver
Abstract:
We report on the optical characterization of non-polar a-plane InGaN quantum dots (QDs) grown by metal-organic vapor phase epitaxy using a short nitrogen anneal treatment at the growth temperature. Spatial and spectral mapping of sub-surface QDs have been achieved by cathodoluminescence at 8 K. Microphotoluminescence studies of the QDs reveal resolution limited sharp peaks with typical linewidth o…
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We report on the optical characterization of non-polar a-plane InGaN quantum dots (QDs) grown by metal-organic vapor phase epitaxy using a short nitrogen anneal treatment at the growth temperature. Spatial and spectral mapping of sub-surface QDs have been achieved by cathodoluminescence at 8 K. Microphotoluminescence studies of the QDs reveal resolution limited sharp peaks with typical linewidth of 1 meV at 4.2 K. Time-resolved photoluminescence studies suggest the excitons in these QDs have a typical lifetime of 538 ps, much shorter than that of the c-plane QDs, which is strong evidence of the significant suppression of the internal electric fields.
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Submitted 24 May, 2013;
originally announced May 2013.