Data Analytics and What It Means to the Materials Community Proceedings of a Workshop (2021) / Chapter Skim
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3 Materials Design
3 Materials Design
Pages 13-28
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From page 13... ...
DATA ANALYTICS IN THE ASM COMMUNITY: BRIDGING MATERIALS SCIENCE AND DATA SCIENCE Mahoney shared ASM's strategies to retain relevance in 21st-century materials market development, collaborate with strategic partners, and address market challenges. By bridging materials science and data science, Mahoney sees an important role for ASM in creating "Materials 4.0," which he considers the backbone for concepts of "Industry 4.0" or "Manufacturing 4.0." Retaining Relevance as a Leader in the Materials Science Marketplace After a rocky period, ASM is in a phase of renewal with a focus on taking a leadership position in the materials science marketplace.
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Ecosystem, an integrated computational materials engineering (ICME) model with data and tools that enables customers to conveniently find value in the Data Lake, a secure repository of all of ASM's data that enables data and metadata experiments and analysis (Figure 3.1)
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Addressing Market Challenges Before closing, Mahoney outlined key challenges including the sheer amount of data; the slow-moving and fragmented state of the materials data market; the
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To do that, he said it must offer data in a variety of forms and formats, convenient and affordable subscriptions, and better overall data access. USING AI TO DISCOVER AND DESIGN MATERIALS Wolverton discussed key challenges in materials informatics, what is being done to overcome them, and areas for future study.
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SOURCE: Chris Wolverton, Northwestern University, presentation to the workshop, from Logan Ward, 2017, "Machine Learning for Materials Discovery and Design," doctoral dissertation, Northwestern Univer sity, used with permission. Data Challenges To improve access to materials science data, Northwestern University has been assembling the Open Quantum Materials Database (OQMD)
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Wolverton and col leagues examined the connectivity of this network, enabling novel questions about compound connections, stability, nobility, and reactivity of materials.3 Another illustration of the application of network theory to materials discovery involved creation of a time-stamped convex hull network (based on the date of the first report of a compound's discovery) , allowing one to make predictions about how feasible it will be to synthesize certain materials in the future.4 Representation Challenges The representation, a set of quantitative attributes that describe a material, is key to effective use of ML in materials science, Wolverton said.
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Using ML to predict crystal structures would be an interesting avenue to explore, he continued, because the current tools for DFT crystal structure prediction are computationally expensive and too slow for high-throughput approaches.
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FIGURE 3.3 Integrated computational materials engineering (ICME) is an approach to the design of products and the materials that comprise them by linking experimentally validated materials models at multiple length scales.
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The Vision 2040 Ecosystem Vision 2040 identifies nine key element domains that ultimately should be interwoven: models and methodologies, multiscale measurement and charac terization tools and methods, optimization and optimization methodologies, decision making and uncertainty quantification and management, verification and validation, data and information and visualization, workflows and collabo ration frameworks, education and training, and computational infrastructure (Figure 3.4)
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NASA's Revolutionary Tools and Methods (RTM) "swim lanes" framework outlines how integrating from first principles to macro materials design -- and data-driven and physics-based approaches -- will enable rapid materials discovery and lead to design and certifica tion of advanced manufacturing-based materials and structures.
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As a result, NASA is building model, microstructure, and software tools tables along with script-driven, generic computational frameworks to support ICME and increase testing speed with zero data loss. Q&A Wolverton asked if high energy density batteries were being pursued to electrify aircraft.
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Mauro identified several key challenges for data science in materials, including data integrity, data security, and creating and maintaining dynamically updated models. He expressed confidence that the technical challenges can be overcome but suggested that changing institutional culture presents a significant challenge to widespread adoption of data science methods in materials design.
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Levi asked if ML can provide insights on structural changes. Mauro replied that ML models can be based on different sets of predictors, including chemical structures such as individual oxides.
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Mauro replied that researchers are still learning about each scale -- such as electronic, structural, microstructural -- and a multiscale approach that incorporates physics and ML represents an excellent candidate for a Grand Challenge for the field. Another participant asked about using ML for composite materials.
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Pollock asked if an optimization problem is also at play. Mauro replied that materials designers can either seek materials that meet certain requirements -- recognizing that in practice, the target frequently changes, resulting in a long, frus trating, and decidedly nonoptimized research cycle -- or they can build modeling tools based on experiments, physics, and ML that can adapt to changing require ments more quickly -- an approach that, in his view, capitalizes on the true value ML offers for materials design.
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Morgan added that it is possible to work together and create an open innovation infrastructure. Having clear targets and publicly available data created improved fuel cell catalysts, for example.
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