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  • 1
    Online Resource
    Online Resource
    Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities
    Springer Science and Business Media LLC ; 2021
    In:  Nature Reviews Physics Vol. 3, No. 10 ( 2021-07-28), p. 685-697
    Details
    In: Nature Reviews Physics, Springer Science and Business Media LLC, Vol. 3, No. 10 ( 2021-07-28), p. 685-697
    Type of Medium: Online Resource
    ISSN: 2522-5820
    URL: Article
    DOI: 10.1038/s42254-021-00345-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2947490-5
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  • 2
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    Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-10-19)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-10-19)
    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.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-74394-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 3
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    Advanced stationary and nonstationary kernel designs for domain-aware Gaussian processes
    Mathematical Sciences Publishers ; 2022
    In:  Communications in Applied Mathematics and Computational Science Vol. 17, No. 1 ( 2022-10-7), p. 131-156
    Details
    In: Communications in Applied Mathematics and Computational Science, Mathematical Sciences Publishers, Vol. 17, No. 1 ( 2022-10-7), p. 131-156
    Type of Medium: Online Resource
    ISSN: 2157-5452 , 1559-3940
    URL: Issue
    URL: Journal
    URL: Article
    DOI: 10.2140/camcos.2022.17-1
    DOI: 10.2140/camcos
    DOI: 10.2140/camcos.2022.17.131
    Language: English
    Publisher: Mathematical Sciences Publishers
    Publication Date: 2022
    detail.hit.zdb_id: 2270595-8
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  • 4
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    Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends
    American Association for the Advancement of Science (AAAS) ; 2023
    In:  Science Advances Vol. 9, No. 2 ( 2023-01-11)
    Details
    In: Science Advances, American Association for the Advancement of Science (AAAS), Vol. 9, No. 2 ( 2023-01-11)
    Abstract: Combinatorial sampling, synchrotron X-ray scattering, and autonomous steering enable rapid discovery of self-assembled phases.
    Type of Medium: Online Resource
    ISSN: 2375-2548
    URL: Article
    DOI: 10.1126/sciadv.add3687
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 2810933-8
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  • 5
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    Autonomous scanning probe microscopy investigations over WS2 and Au{111}
    Springer Science and Business Media LLC ; 2022
    In:  npj Computational Materials Vol. 8, No. 1 ( 2022-05-02)
    Details
    In: npj Computational Materials, Springer Science and Business Media LLC, Vol. 8, No. 1 ( 2022-05-02)
    Abstract: Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS 2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS 2 , Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.
    Type of Medium: Online Resource
    ISSN: 2057-3960
    URL: Article
    DOI: 10.1038/s41524-022-00777-9
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2843287-3
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  • 6
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    Machine-learning for designing nanoarchitectured materials by dealloying
    Springer Science and Business Media LLC ; 2022
    In:  Communications Materials Vol. 3, No. 1 ( 2022-11-09)
    Details
    In: Communications Materials, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2022-11-09)
    Abstract: Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.
    Type of Medium: Online Resource
    ISSN: 2662-4443
    URL: Article
    DOI: 10.1038/s43246-022-00303-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 3008524-X
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  • 7
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    K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy
    IOP Publishing ; 2020
    In:  Machine Learning: Science and Technology Vol. 1, No. 4 ( 2020-12-01), p. 045015-
    Details
    In: Machine Learning: Science and Technology, IOP Publishing, Vol. 1, No. 4 ( 2020-12-01), p. 045015-
    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.
    Type of Medium: Online Resource
    ISSN: 2632-2153
    URL: Article
    DOI: 10.1088/2632-2153/abab61
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 3017004-7
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  • 8
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    Surface enrichment dictates block copolymer orientation
    Royal Society of Chemistry (RSC) ; 2023
    In:  Nanoscale Vol. 15, No. 15 ( 2023), p. 6901-6912
    Details
    In: Nanoscale, Royal Society of Chemistry (RSC), Vol. 15, No. 15 ( 2023), p. 6901-6912
    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.
    Type of Medium: Online Resource
    ISSN: 2040-3364 , 2040-3372
    URL: Article
    DOI: 10.1039/D3NR00095H
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2023
    detail.hit.zdb_id: 2515664-0
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  • 9
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    Advances in Kriging-Based Autonomous X-Ray Scattering Experiments
    Springer Science and Business Media LLC ; 2020
    In:  Scientific Reports Vol. 10, No. 1 ( 2020-01-28)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-01-28)
    Abstract: Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-020-57887-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2615211-3
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  • 10
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    Mathematical nuances of Gaussian process-driven autonomous experimentation
    Springer Science and Business Media LLC ; 2023
    In:  MRS Bulletin Vol. 48, No. 2 ( 2023-02), p. 153-163
    Details
    In: MRS Bulletin, Springer Science and Business Media LLC, Vol. 48, No. 2 ( 2023-02), p. 153-163
    Abstract: The fields of machine learning (ML) and artificial intelligence (AI) have transformed almost every aspect of science and engineering. The excitement for AI/ML methods is in large part due to their perceived novelty, as compared to traditional methods of statistics, computation, and applied mathematics. But clearly, all methods in ML have their foundations in mathematical theories, such as function approximation, uncertainty quantification, and function optimization. Autonomous experimentation is no exception; it is often formulated as a chain of off-the-shelf tools, organized in a closed loop, without emphasis on the intricacies of each algorithm involved. The uncomfortable truth is that the success of any ML endeavor, and this includes autonomous experimentation, strongly depends on the sophistication of the underlying mathematical methods and software that have to allow for enough flexibility to consider functions that are in agreement with particular physical theories. We have observed that standard off-the-shelf tools, used by many in the applied ML community, often hide the underlying complexities and therefore perform poorly. In this paper, we want to give a perspective on the intricate connections between mathematics and ML, with a focus on Gaussian process-driven autonomous experimentation. Although the Gaussian process is a powerful mathematical concept, it has to be implemented and customized correctly for optimal performance. We present several simple toy problems to explore these nuances and highlight the importance of mathematical and statistical rigor in autonomous experimentation and ML. One key takeaway is that ML is not, as many had hoped, a set of agnostic plug-and-play solvers for everyday scientific problems, but instead needs expertise and mastery to be applied successfully. Graphical abstract
    Type of Medium: Online Resource
    ISSN: 0883-7694 , 1938-1425
    URL: Article
    DOI: 10.1557/s43577-023-00478-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2749565-6
    detail.hit.zdb_id: 2136359-6
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