دورية أكاديمية
Explainability and human intervention in autonomous scanning probe microscopy.
| العنوان: | Explainability and human intervention in autonomous scanning probe microscopy. |
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| المؤلفون: | Liu Y; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA., Ziatdinov MA; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA., Vasudevan RK; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA., Kalinin SV; Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA. |
| المصدر: | Patterns (New York, N.Y.) [Patterns (N Y)] 2023 Oct 09; Vol. 4 (11), pp. 100858. Date of Electronic Publication: 2023 Oct 09 (Print Publication: 2023). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Inc Country of Publication: United States NLM ID: 101767765 Publication Model: eCollection Cited Medium: Internet ISSN: 2666-3899 (Electronic) Linking ISSN: 26663899 NLM ISO Abbreviation: Patterns (N Y) Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: [New York] : Elsevier Inc., [2020]- |
| مستخلص: | The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis. Competing Interests: The authors declare no conflict of interest. (© 2023 The Author(s), Oak Ridge National Laboratory.) |
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| فهرسة مساهمة: | Keywords: Gaussian process; autonomous experiments; deep kernel learning; human in the loop; scanning probe microscopy |
| تواريخ الأحداث: | Date Created: 20231130 Latest Revision: 20231202 |
| رمز التحديث: | 20231202 |
| مُعرف محوري في PubMed: | PMC10682748 |
| DOI: | 10.1016/j.patter.2023.100858 |
| PMID: | 38035198 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2666-3899 |
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| DOI: | 10.1016/j.patter.2023.100858 |