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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.15083 |
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| _version_ | 1866909358084849664 |
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| author | Mishra, Saswat Karumuri, Sharmila Mika, Vincent Scott, Collin Choy, Chadwick Sandhage, Kenneth H. Bilionis, Ilias Titus, Michael S. Strachan, Alejandro |
| author_facet | Mishra, Saswat Karumuri, Sharmila Mika, Vincent Scott, Collin Choy, Chadwick Sandhage, Kenneth H. Bilionis, Ilias Titus, Michael S. Strachan, Alejandro |
| contents | The area-normalized change of mass ($Δ$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. These numbers were obtained assuming an experimental noise of 2.5% of the mass gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is open access and researchers can add their own raw data (those to be included in future publications, as well as negative results) for analysis and to share their work with the community. Such consistent and systematic analysis of open, community generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15083 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing Mishra, Saswat Karumuri, Sharmila Mika, Vincent Scott, Collin Choy, Chadwick Sandhage, Kenneth H. Bilionis, Ilias Titus, Michael S. Strachan, Alejandro Materials Science The area-normalized change of mass ($Δ$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. These numbers were obtained assuming an experimental noise of 2.5% of the mass gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is open access and researchers can add their own raw data (those to be included in future publications, as well as negative results) for analysis and to share their work with the community. Such consistent and systematic analysis of open, community generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance. |
| title | Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing |
| topic | Materials Science |
| url | https://arxiv.org/abs/2310.15083 |