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1. Assessing predictability of environmental time series with statistical and machine learning models

   https://doi.org/10.1002/env.2864  

   Bonas, Matthew; Datta, Abhirup; Wikle, Christopher_K; Boone, Edward_L; Alamri, Faten_S; Hari, Bhava_Vyasa; Kavila, Indulekha; Simmons, Susan_J; Jarvis, Shannon_M; Burr, Wesley_S; et al (July 2024, Environmetrics)

   Abstract The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model‐based approach with formal uncertainty quantification. Should, however, classical statistical methodologies be discarded altogether in environmental statistics, and should our contribution be focused on formalizing machine learning constructs? This work aims at providing some answers to this thought‐provoking question with two time series case studies where selected models from both the statistical and machine learning literature are compared in terms of forecasting skills, uncertainty quantification and computational time. Relative merits of both class of approaches are discussed, and broad open questions are formulated as a baseline for a discussion on the topic. 

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2. Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

   https://doi.org/10.1111/jace.16677  

   De_Guire, Eileen; Bartolo, Laura; Brindle, Ross; Devanathan, Ram; Dickey, Elizabeth_C; Fessler, Justin; French, Roger_H; Fotheringham, Ulrich; Harmer, Martin; Lara‐Curzio, Edgar; et al (July 2019, Journal of the American Ceramic Society)

   Abstract Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development. 

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3. Challenges in Information-Mining the Materials Literature: A Case Study and Perspective

   https://doi.org/10.1021/acs.chemmater.2c00445  

   Smith, Andrew; Bhat, Vinayak; Ai, Qianxiang; Risko, Chad (May 2022, Chemistry of Materials)

   The rapid development and application of machine learning (ML) techniques in materials science have led to new tools for machine-enabled and autonomous/high-throughput materials design and discovery. Alongside, efforts to extract data from traditional experiments in the published literature with natural language processing (NLP) algorithms provide opportunities to develop tremendous data troves for these in silico design and discovery endeavors. While NLP is used in all aspects of society, its application in materials science is still in the very early stages. This perspective provides a case study on the application of NLP to extract information related to the preparation of organic materials. We present the case study at a basic level with the aim to discuss these technologies and processes with researchers from diverse scientific backgrounds. We also discuss the challenges faced in the case study and provide an assessment to improve the accuracy of NLP techniques for materials science with the aid of community contributions. 

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4. Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery

   https://doi.org/10.1016/j.patter.2023.100863  

   Qian, Xiaoning; Yoon, Byung-Jun; Arróyave, Raymundo; Qian, Xiaofeng; Dougherty, Edward R. (November 2023, Patterns)

   Thanks to the rapid advances in artificial intelligence, AI for science (AI4Science) has emerged as one of the new promising research directions for modern science and engineering. In this review, we focus on recent efforts to develop knowledge-driven Bayesian learning and experimental design methods for accelerating the discovery of novel functional materials as well as enhancing the understanding of composition-process-structure-property relationships. We specifically discuss the challenges and opportunities in integrating prior scientific knowledge and physics principles with AI and machine learning (ML) models for accelerating materials and knowledge discovery. The current state-of-the-art methods in knowledge-based prior construction, model fusion, uncertainty quantification, optimal experimental design, and symbolic regression are detailed in the review, along with several detailed case studies and results in materials discovery. 

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5. Promoting shifts in teachers’ understanding and use of phenomena in instruction and assessment

   Lo, Abraham S.; Glidewell, Loraine; O’Connor, Keelin; Allen, Anna-Ruth; Herrmann-Abell, Cari F.; Penuel, William R.; Wingert, Kerri; Lindsay, William (July 2022, Computersupported collaborative learning)

   Chinn, Clark; Tan, Edna; Chan, Carol K.; Kali, Yael (Ed.)

   This paper examines an online professional learning intervention to develop teachers’ pedagogical design capacity to develop five-dimensional (5D) learning and assessment opportunities, which involve integrated use of science and engineering practices, disciplinary core ideas, and crosscutting concepts in science to make sense of phenomena and problems that are interesting to students and support students as knowers, doers, and users of science. We present findings from our design study, which suggest both the promise of such an approach and some of the challenges and tensions experienced by teachers as they chose and used phenomena to support 5D learning opportunities for students. 

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