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Nabika T, Nagata K, Mizumaki M, Katakami S, Okada M. Bayesian active learning with model selection for spectral experiments. Sci Rep 2024; 14:3680. [PMID: 38355775 PMCID: PMC10866988 DOI: 10.1038/s41598-024-54329-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 02/11/2024] [Indexed: 02/16/2024] Open
Abstract
Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments. Therefore, we proposed an active learning with model selection method using multiple parametric models as learning models. Important points for model selection and its parameter estimation were actively measured using Bayesian posterior distribution. The present study demonstrated the effectiveness of our proposed method for spectral deconvolution and Hamiltonian selection in X-ray photoelectron spectroscopy.
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Affiliation(s)
- Tomohiro Nabika
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Kenji Nagata
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki, 305-0047, Japan
| | - Masaichiro Mizumaki
- Faculty of Science, Course for Physical Sciences, Kumamoto University, Kumamoto, Japan
| | - Shun Katakami
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Masato Okada
- Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan.
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2
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Kandel S, Zhou T, Babu AV, Di Z, Li X, Ma X, Holt M, Miceli A, Phatak C, Cherukara MJ. Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy. Nat Commun 2023; 14:5501. [PMID: 37679317 PMCID: PMC10485018 DOI: 10.1038/s41467-023-40339-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 07/19/2023] [Indexed: 09/09/2023] Open
Abstract
Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.
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Affiliation(s)
- Saugat Kandel
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA.
| | - Tao Zhou
- Nanoscience and Technology Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | | | - Zichao Di
- Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Xinxin Li
- Nanoscience and Technology Division, Argonne National Laboratory, Lemont, IL, 60439, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, 60637, USA
| | - Xuedan Ma
- Nanoscience and Technology Division, Argonne National Laboratory, Lemont, IL, 60439, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, 60637, USA
| | - Martin Holt
- Nanoscience and Technology Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Antonino Miceli
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Charudatta Phatak
- Materials Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Mathew J Cherukara
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA.
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3
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Bae S, Noack MM, Yager KG. Surface enrichment dictates block copolymer orientation. NANOSCALE 2023; 15:6901-6912. [PMID: 36876525 DOI: 10.1039/d3nr00095h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Orientation of block copolymer (BCP) morphology in thin films is critical to applications as nanostructured coatings. Despite being well-studied, the ability to control BCP orientation across all possible block constituents remains challenging. Here, we deploy coarse-grained molecular dynamics simulations to study diblock copolymer ordering in thin films, focusing on chain makeup, substrate surface energy, and surface tension disparity between the two constituent blocks. We explore the multi-dimensional parameter space of ordering using a machine-learning approach, where an autonomous loop using a Gaussian process (GP) control algorithm iteratively selects high-value simulations to compute. The GP kernel was engineered to capture known symmetries. The trained GP model serves as both a complete map of system response, and a robust means of extracting material knowledge. We demonstrate that the vertical orientation of BCP phases depends on several counter-balancing energetic contributions, including entropic and enthalpic material enrichment at interfaces, distortion of morphological objects through the film depth, and of course interfacial energies. BCP lamellae are found more resistant to these effects, and thus more robustly form vertical orientations across a broad range of conditions; while BCP cylinders are found to be highly sensitive to surface tension disparity.
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Affiliation(s)
- Suwon Bae
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA.
| | - Marcus M Noack
- The Center for Advanced Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Kevin G Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York 11973, USA.
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4
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Liu Y, Morozovska AN, Eliseev EA, Kelley KP, Vasudevan R, Ziatdinov M, Kalinin SV. Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. PATTERNS (NEW YORK, N.Y.) 2023; 4:100704. [PMID: 36960442 PMCID: PMC10028429 DOI: 10.1016/j.patter.2023.100704] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/18/2022] [Accepted: 02/09/2023] [Indexed: 03/12/2023]
Abstract
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore the bias-induced transformations that underpin the functionality of broad classes of devices and materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of a broad range of control parameters, leading to experimentally intractable scenarios. Meanwhile, often these behaviors are understood within potentially competing theoretical hypotheses. Here, we develop a hypothesis list covering possible limiting scenarios for domain growth in ferroelectric materials, including thermodynamic, domain-wall pinning, and screening limited. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching, and the results indicate that domain growth is ruled by kinetic control. We note that the hypothesis learning can be broadly used in other automated experiment settings.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Corresponding author
| | - Anna N. Morozovska
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine
| | - Eugene A. Eliseev
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine
- Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, Krjijanovskogo 3, 03142 Kyiv, Ukraine
| | - Kyle P. Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
| | - Rama Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Corresponding author
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Corresponding author
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5
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Doerk GS, Stein A, Bae S, Noack MM, Fukuto M, Yager KG. Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends. SCIENCE ADVANCES 2023; 9:eadd3687. [PMID: 36638174 PMCID: PMC9839324 DOI: 10.1126/sciadv.add3687] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA.
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Affiliation(s)
- Gregory S. Doerk
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Aaron Stein
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Suwon Bae
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Marcus M. Noack
- The Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Masafumi Fukuto
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Kevin G. Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA
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Yano J, Gaffney KJ, Gregoire J, Hung L, Ourmazd A, Schrier J, Sethian JA, Toma FM. The case for data science in experimental chemistry: examples and recommendations. Nat Rev Chem 2022; 6:357-370. [PMID: 37117931 DOI: 10.1038/s41570-022-00382-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 12/31/2022]
Abstract
The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.
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Smilgies D. GISAXS
: A versatile tool to assess structure and self‐assembly kinetics in block copolymer thin films. JOURNAL OF POLYMER SCIENCE 2022. [DOI: 10.1002/pol.20210244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Detlef‐M. Smilgies
- Center for Advanced Microelectronics Manufacturing (CAMM) Binghamton University Binghamton New York USA
- School of Pharmacy and Pharmaceutical Sciences Binghamton University Binghamton New York USA
- Materials Science and Engineering Program Binghamton University Binghamton New York USA
- R.F. Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca New York USA
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Kalinin SV, Ziatdinov M, Hinkle J, Jesse S, Ghosh A, Kelley KP, Lupini AR, Sumpter BG, Vasudevan RK. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. ACS NANO 2021; 15:12604-12627. [PMID: 34269558 DOI: 10.1021/acsnano.1c02104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.
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Vasudevan RK, Kelley KP, Hinkle J, Funakubo H, Jesse S, Kalinin SV, Ziatdinov M. Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics. ACS NANO 2021; 15:11253-11262. [PMID: 34228427 DOI: 10.1021/acsnano.0c10239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.
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Affiliation(s)
- Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Jacob Hinkle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Maxim Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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10
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Toth K, Osuji CO, Yager KG, Doerk GS. High-throughput morphology mapping of self-assembling ternary polymer blends. RSC Adv 2020; 10:42529-42541. [PMID: 35516747 PMCID: PMC9057993 DOI: 10.1039/d0ra08491c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 11/13/2020] [Indexed: 11/23/2022] Open
Abstract
Multicomponent blending is a convenient yet powerful approach to rationally control the material structure, morphology, and functional properties in solution-deposited films of block copolymers and other self-assembling nanomaterials. However, progress in understanding the structural and morphological dependencies on blend composition is hampered by the time and labor required to synthesize and characterize a large number of discrete samples. Here, we report a new method to systematically explore a wide composition space in ternary blends. Specifically, the blend composition space is divided into gradient segments deposited sequentially on a single wafer by a new gradient electrospray deposition tool, and characterized using high-throughput grazing-incidence small-angle X-ray scattering. This method is applied to the creation of a ternary morphology diagram for a cylinder-forming polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) block copolymer blended with PS and PMMA homopolymers. Using “wet brush” homopolymers of very low molecular weight (∼1 kg mol−1), we identify well-demarcated composition regions comprising highly ordered cylinder, lamellae, and sphere morphologies, as well as a disordered phase at high homopolymer mass fractions. The exquisite granularity afforded by this approach also helps to uncover systematic dependencies among self-assembled morphology, topological grain size, and domain period as functions of homopolymer mass fraction and PS : PMMA ratio. These results highlight the significant advantages afforded by blending low molecular weight homopolymers for block copolymer self-assembly. Meanwhile, the high-throughput, combinatorial approach to investigating nanomaterial blends introduced here dramatically reduces the time required to explore complex process parameter spaces and is a natural complement to recent advances in autonomous X-ray characterization. Compositionally graded electrospray deposition combined with grazing incidence small angle X-ray scattering forms a high-throughput approach for mapping phase behavior in ternary mixtures as demonstrated here using block copolymer blends.![]()
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Affiliation(s)
- Kristof Toth
- Department of Chemical and Environmental Engineering, Yale University New Haven Connecticut 06520 USA
| | - Chinedum O Osuji
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania Philadelphia Pennsylvania 19104 USA
| | - Kevin G Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory Upton New York 11973 USA
| | - Gregory S Doerk
- Center for Functional Nanomaterials, Brookhaven National Laboratory Upton New York 11973 USA
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Noack MM, Doerk GS, Li R, Streit JK, Vaia RA, Yager KG, Fukuto M. Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels. Sci Rep 2020; 10:17663. [PMID: 33077759 PMCID: PMC7573639 DOI: 10.1038/s41598-020-74394-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 09/30/2020] [Indexed: 11/09/2022] Open
Abstract
A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.
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Affiliation(s)
- Marcus M Noack
- The Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
| | - Gregory S Doerk
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Ruipeng Li
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Jason K Streit
- Materials and Manufacturing Directorate, Air Force Research Laboratories, Wright-Patterson Air Force Base, OH, 45433, USA
| | - Richard A Vaia
- Materials and Manufacturing Directorate, Air Force Research Laboratories, Wright-Patterson Air Force Base, OH, 45433, USA
| | - Kevin G Yager
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY, 11973, USA.
| | - Masafumi Fukuto
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, 11973, USA.
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Melton CN, Noack MM, Ohta T, Beechem TE, Robinson J, Zhang X, Bostwick A, Jozwiak C, Koch RJ, Zwart PH, Hexemer A, Rotenberg E. K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/abab61] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.
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