Data Analytics and What It Means to the Materials Community Proceedings of a Workshop (2021) / Chapter Skim
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2 Keynote Addresses
2 Keynote Addresses
Pages 5-12
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The Promise of Machine Learning ML has enormous promise for the materials community. Taking advantage of experimental, computational, and analytical sources of data, ML algorithms can quickly discover unexpected correlations between material properties, dramatically reduce the number of experiments required, and reduce overall costs.
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In one project, Intellegens merged multiple data sources to help Rolls-Royce find a new alloy that could withstand jet engine temperatures, be three-dimensional (3D) -printable with direct laser deposition, and meet other performance p arameters for factors such as cost and fatigue.
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He noted that Intellegens has created an integrated software product that allows customers to load their own data, train the ML model, and design a new material all in one workflow that can be shared by individual scientists and business managers, in addition to being shared between scientists to foster new collaborations. Any ML model is reliant on sources of data that can be used to train the model; one important barrier in the materials field is that data is siloed in separate databases.
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While future technologies may enable process-property capabilities, he believes structure remains an essential component of the workflow today. PSP linkages cover a hierarchy of several material structure scales, each of which has its own variables at the macro-, meso-, and microscale -- one reason why materials science is so complex.
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The first relates to microstructure quantification. K alidindi and colleagues successfully employed advanced pattern recognition for micro structures through fast computations of n-point spatial correlations on large data sets.3 They achieved this by taking advantage of discrete Fourier transforms, which 3 S.
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The team also applied its fast and efficient statistical distribution computations at the atomic scale, converting coordinates from public databases to a voxelated microstructure. This in turn enables a feature engineering approach in which, using filters, features of interest can be defined in a rigorous statistical framework.
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12 For localization, the team has applied its approach to study stress fields in polycrystals, creating predictions in under a minute on a standard desktop computer.6 The process also works for predicting plastic strain rates in two-phase composites.7 Kalidindi noted that localization studies are important because they provide an opportunity for learning about fatigue performance without running expensive simulations.8 After highlighting various ways to extract material properties at the microscale and macroscale, Kalidindi concluded by emphasizing the value of AI-based materials knowledge systems to provide objective decision support for materials innovation, as well as the continued need for high-throughput, cost-effective, multiresolution mechanical measurement protocols to generate the data to feed into these knowledge systems.
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Ward Plummer, Louisiana State University, asked how the materials knowl edge system framework interfaces with specific parameters set by materials users. Kalidindi stressed that any such work is a collaboration, and that his team must work with the appropriate domain experts to select baseline materials and target definitions in order for the approach to yield useful results.
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