A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films Article Swipe
Kimberly L. Bassett
,
Tylan Watkins
,
Jonathan Coleman
,
Nathan Bianco
,
Lauren S. Bailey
,
Jamin Ryan Pillars
,
Samuel Garrett Williams
,
Tomas F. Babuska
,
John F. Curry
,
Frank W. DelRio
,
Amelia Henriksen
,
Anthony Garland
,
Justin Hall
,
Brandon A. Krick
,
Brad Boyce
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s40192-023-00315-5
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s40192-023-00315-5
Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40192-023-00315-5
- https://link.springer.com/content/pdf/10.1007/s40192-023-00315-5.pdf
- OA Status
- hybrid
- Cited By
- 10
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388203602
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388203602Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40192-023-00315-5Digital Object Identifier
- Title
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A Workflow for Accelerating Multimodal Data Collection for Electrodeposited FilmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-26Full publication date if available
- Authors
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Kimberly L. Bassett, Tylan Watkins, Jonathan Coleman, Nathan Bianco, Lauren S. Bailey, Jamin Ryan Pillars, Samuel Garrett Williams, Tomas F. Babuska, John F. Curry, Frank W. DelRio, Amelia Henriksen, Anthony Garland, Justin Hall, Brandon A. Krick, Brad BoyceList of authors in order
- Landing page
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https://doi.org/10.1007/s40192-023-00315-5Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s40192-023-00315-5.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s40192-023-00315-5.pdfDirect OA link when available
- Concepts
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Workflow, Computer science, Characterization (materials science), Automation, Acceleration, Data collection, Artificial intelligence, Process engineering, Materials science, Nanotechnology, Mechanical engineering, Database, Engineering, Mathematics, Physics, Classical mechanics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 6, 2024: 4Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.amenable | 121 |
| abstract_inverted_index.critical | 45 |
| abstract_inverted_index.describe | 78 |
| abstract_inverted_index.employed | 62 |
| abstract_inverted_index.enhances | 34 |
| abstract_inverted_index.evaluate | 58 |
| abstract_inverted_index.learning | 3 |
| abstract_inverted_index.research | 15, 125 |
| abstract_inverted_index.testing, | 156 |
| abstract_inverted_index.thinking | 46 |
| abstract_inverted_index.workflow | 81, 134 |
| abstract_inverted_index.(friction | 153 |
| abstract_inverted_index.13–46x. | 163 |
| abstract_inverted_index.academic, | 128 |
| abstract_inverted_index.addition, | 165 |
| abstract_inverted_index.automated | 166 |
| abstract_inverted_index.difficult | 73 |
| abstract_inverted_index.elaborate | 63 |
| abstract_inverted_index.materials | 6, 59, 124 |
| abstract_inverted_index.minimizes | 29 |
| abstract_inverted_index.resources | 117 |
| abstract_inverted_index.resulting | 99 |
| abstract_inverted_index.sampling, | 36 |
| abstract_inverted_index.automation | 22 |
| abstract_inverted_index.autonomous | 17 |
| abstract_inverted_index.commercial | 131 |
| abstract_inverted_index.facilities | 126 |
| abstract_inverted_index.individual | 107 |
| abstract_inverted_index.multimodal | 25, 85 |
| abstract_inverted_index.principles | 175 |
| abstract_inverted_index.repetitive | 49 |
| abstract_inverted_index.repository | 171 |
| abstract_inverted_index.scientists | 39 |
| abstract_inverted_index.strategies | 4, 70, 112 |
| abstract_inverted_index.synthesize | 56 |
| abstract_inverted_index.accelerated | 19, 24, 177 |
| abstract_inverted_index.approaches. | 20 |
| abstract_inverted_index.generalize. | 75 |
| abstract_inverted_index.government, | 129 |
| abstract_inverted_index.implemented | 80 |
| abstract_inverted_index.microscopy, | 143 |
| abstract_inverted_index.modalities: | 141 |
| abstract_inverted_index.specialized | 69 |
| abstract_inverted_index.statistical | 35 |
| abstract_inverted_index.accelerating | 83 |
| abstract_inverted_index.acceleration | 53, 111, 137 |
| abstract_inverted_index.demonstrated | 135 |
| abstract_inverted_index.diffraction, | 147 |
| abstract_inverted_index.laboratories | 66 |
| abstract_inverted_index.optimization | 8 |
| abstract_inverted_index.self-driving | 65 |
| abstract_inverted_index.tribological | 152 |
| abstract_inverted_index.combinatorial | 89 |
| abstract_inverted_index.electroplated | 93 |
| abstract_inverted_index.fluorescence, | 149 |
| abstract_inverted_index.laboratories. | 132 |
| abstract_inverted_index.profilometry, | 145 |
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| abstract_inverted_index.nanoindentation, | 150 |
| abstract_inverted_index.capital-intensive | 13 |
| abstract_inverted_index.manufacturing-scale | 116 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.76004748 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |