{"title":"Beyond Combinatorial Materials Science: The 100 Prisoners Problem","authors":"","doi":"10.1007/s40192-023-00330-6","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Advancements in high-throughput data generation and physics-informed artificial intelligence and machine-learning algorithms are rapidly challenging the status quo for how materials data is collected, analyzed, and communicated with the world. Machine-learning algorithms can be executed in just a few lines of code by researchers with minimal data science expertise. This perspective addresses the reality that the ecosystems which have been constructed to nurture new materials discovery and development are not yet well equipped to take advantage of the radically more powerful and accessible computational and algorithmic tools which have the immediate potential to enhance the pace of scientific advancement in this field. A novel architecture for managing materials data is proposed and discussed from the standpoint of how historical and emerging subfields of materials science could have been or might still significantly improve the impact of materials discoveries to the many human societal needs for new materials.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"1 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrating Materials and Manufacturing Innovation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1007/s40192-023-00330-6","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Advancements in high-throughput data generation and physics-informed artificial intelligence and machine-learning algorithms are rapidly challenging the status quo for how materials data is collected, analyzed, and communicated with the world. Machine-learning algorithms can be executed in just a few lines of code by researchers with minimal data science expertise. This perspective addresses the reality that the ecosystems which have been constructed to nurture new materials discovery and development are not yet well equipped to take advantage of the radically more powerful and accessible computational and algorithmic tools which have the immediate potential to enhance the pace of scientific advancement in this field. A novel architecture for managing materials data is proposed and discussed from the standpoint of how historical and emerging subfields of materials science could have been or might still significantly improve the impact of materials discoveries to the many human societal needs for new materials.
期刊介绍：
The journal will publish: Research that supports building a model-based definition of materials and processes that is compatible with model-based engineering design processes and multidisciplinary design optimization; Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are to be used for ICME; Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and exchange of data; In-depth descriptions of data, databases, and database tools; Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing.
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