Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows

Abstract

Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the materials genome initiative (MGI) is most effectively pursued within the vision of a materials innovation ecosystem that spans across conventional engineering, science, and computing disciplines. The ICME foundation integrates principles of materials science with computational methods, including increasing reliance on modern data science methods that are savvy to digital information that recognizes hierarchical material structure and the need for correlative relations for process-structure and structure-property relations. We consider gaps in academic research and education programs related to systems engineering, uncertainty quantification of both experiments and computation, and data science methods. Barriers to the introduction of materials data science are discussed, as well as opportunities for innovation in educating the future MGI and ICME workforce.

Using Jupyter Tools to Design an Interactive Textbook to Guide Undergraduate Research in Materials Informatics

“…Students used this DFT-generated data to train ML models to predict a material’s dielectric constant (κ) and then used the ML model, along with other criteria of their choosing, to screen for the “best” high-κ dielectric material for nanoscale transistors. This research question is an active area of research , with documented impact for the microelectronics industry. , The content also aligned well with the two other modules in the summer research internship, which shows how a modular design can be flexibly sequenced with other curricula, consistent with the recommendations of other educators. ,, All files are freely available on GitHub under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, and the textbook is hosted for free on GitHub Pages . GitHub Pages was chosen for its simplicity and flexibility, and step-by-step instructions for hosting Jupyter Books on a variety of platforms can be found in the documentation…”

“…41,42 The content also aligned well with the two other modules in the summer research internship, which shows how a modular design can be flexibly sequenced with other curricula, consistent with the recommendations of other educators. 8,9,43 All files are freely available on GitHub under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license, and the textbook is hosted for free on GitHub Pages. 28 GitHub Pages was chosen for its simplicity and flexibility, and step-by-step instructions for hosting Jupyter Books on a variety of platforms can be found in the documentation.…”

“…Scientists have leveraged ML/AI technologies to design new materials with the ultimate goal to achieve lower cost, accelerated time to market, and greater sustainability. Now, the maturation of these technologies in research presents exciting opportunities to transfer them into the classroom, where educators can design new informatics curricula to train the next generation of informatics-skilled scientists. − …”

“…The multidisciplinary nature of chemoinformatics calls for a robust curriculum that combines scientific domain knowledge, data science, and computing skills, which is difficult for students to fit into an already strained course load and for an instructor to gain mastery in all components . Due to the comprehensive scope of materials informatics (MI), it has been advised that MI curricula be structured as small modules to facilitate its integration with existing courses and to help alleviate some of the aforementioned problems; , however, this integration presents its own challenges that must be carefully considered during instructional design. For example, it may not be obvious how a research-based tool can fit within the scope of the existing curriculum and whether additional software-specific training is necessary.…”

Designing Evolving Cyber-Physical-Social Systems: Computational Research Opportunities

“…Solution processes for Types III and IV which are effective for early-stage top-down design exploration are presented in [54,[60][61][62]. Nellippallil and co-authors illustrate how to manage all four types of uncertainty when co-designing materials, products and manufacturing processes [46,60] The design literature tends to be focused on the sequential exploration in different modules instead of a concurrent (or co-) realization of systems, especially those from different disciplines [62][63][64][65][66][67][68].…”