MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research
Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research. While this process may seem opaque to researchers outside th...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00787-7 |
| _version_ | 1818251233861828608 |
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| author | Keith A. Brown |
| author_facet | Keith A. Brown |
| author_sort | Keith A. Brown |
| collection | DOAJ |
| description | Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research. While this process may seem opaque to researchers outside the field of machine learning, examining active learning in games provides an accessible way to showcase the process and its virtues. Here, we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research. |
| first_indexed | 2024-12-12T16:05:02Z |
| format | Article |
| id | doaj.art-7a43f03cc201413d803185166bd63b0b |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-12-12T16:05:02Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-7a43f03cc201413d803185166bd63b0b2022-12-22T00:19:20ZengNature Portfolionpj Computational Materials2057-39602022-05-01811310.1038/s41524-022-00787-7MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials researchKeith A. Brown0Department of Mechanical Engineering, Physics Department, and Division of Materials Science & Engineering, Boston UniversityResearch and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research. While this process may seem opaque to researchers outside the field of machine learning, examining active learning in games provides an accessible way to showcase the process and its virtues. Here, we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research.https://doi.org/10.1038/s41524-022-00787-7 |
| spellingShingle | Keith A. Brown MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research npj Computational Materials |
| title | MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research |
| title_full | MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research |
| title_fullStr | MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research |
| title_full_unstemmed | MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research |
| title_short | MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research |
| title_sort | model guess check wordle as a primer on active learning for materials research |
| url | https://doi.org/10.1038/s41524-022-00787-7 |
| work_keys_str_mv | AT keithabrown modelguesscheckwordleasaprimeronactivelearningformaterialsresearch |