Mrinalini Mulukutla, A. Nicole Person, Sven Voigt, Lindsey Kuettner, Branden Kappes, Danial Khatamsaz, Robert Robinson, Daniel Salas Mula, Wenle Xu, Daniel Lewis, Hongkyu Eoh, Kailu Xiao, Haoren Wang, Jaskaran Singh Saini, Raj Mahat, Trevor Hastings, Matthew Skokan, Vahid Attari, Michael Elverud, James D. Paramore, Brady Butler, Kenneth Vecchio, Surya R. Kalidindi, Douglas Allaire, Ibrahim Karaman, Edwin L. Thomas, George Pharr, Ankit Srivastava, Raymundo Arróyave
{"title":"Illustrating an Effective Workflow for Accelerated Materials Discovery","authors":"Mrinalini Mulukutla, A. Nicole Person, Sven Voigt, Lindsey Kuettner, Branden Kappes, Danial Khatamsaz, Robert Robinson, Daniel Salas Mula, Wenle Xu, Daniel Lewis, Hongkyu Eoh, Kailu Xiao, Haoren Wang, Jaskaran Singh Saini, Raj Mahat, Trevor Hastings, Matthew Skokan, Vahid Attari, Michael Elverud, James D. Paramore, Brady Butler, Kenneth Vecchio, Surya R. Kalidindi, Douglas Allaire, Ibrahim Karaman, Edwin L. Thomas, George Pharr, Ankit Srivastava, Raymundo Arróyave","doi":"10.1007/s40192-024-00357-3","DOIUrl":null,"url":null,"abstract":"<p>Algorithmic materials discovery is a multidisciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others’ data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. To further enhance precision and interoperability in this multifaceted research landscape, we must explore innovative ways to refine these processes and improve the integration of expertise and data across diverse domains. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated materials discovery framework as a part of the High-Throughput Materials Discovery for Extreme Conditions (HTMDEC) Initiative. Our <i>BIRDSHOT</i> (Batch-wise Improvement in Reduced Materials Design Space using a Holistic Optimization Technique) Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms.</p>","PeriodicalId":13604,"journal":{"name":"Integrating Materials and Manufacturing Innovation","volume":"34 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-06-03","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/s40192-024-00357-3","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
Algorithmic materials discovery is a multidisciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others’ data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. To further enhance precision and interoperability in this multifaceted research landscape, we must explore innovative ways to refine these processes and improve the integration of expertise and data across diverse domains. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated materials discovery framework as a part of the High-Throughput Materials Discovery for Extreme Conditions (HTMDEC) Initiative. Our BIRDSHOT (Batch-wise Improvement in Reduced Materials Design Space using a Holistic Optimization Technique) Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms.
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.