Frontiers in Data Analytics and Monitoring Tools for Extreme Materials Proceedings of a Workshop (2023) / Chapter Skim
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Pages 28-50
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From page 28... ...
Cao described results from a recent publication on how short-range order within a multi-principle-element material affects the energy landscape of the lattice and, thus, how easily disloca tions can move at different points in the lattice (Wang et al.
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From page 29... ...
They showed that the machine learning model, which was a convolutional neural network, was able to successfully predict diffusivity for other compositions involving tantalum, niobium, and molybdenum that were not part of the training set (Fan et al.
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From page 30... ...
He mentioned that a second way in which machine learning is valuable in working with multi-principal-element materials is in accessing the materials' com positional complexity. As the number of elements in a material increase, the num ber of possible compositions and structures explodes -- the so-called "combinato rial explosion" -- and creates a huge space of potential compositions that must be considered in looking for materials with optimal properties for extreme conditions.
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Opila, University of Virginia. Benchmarking Machine Learning Modeling Approaches Aaron Stebner, Georgia Tech, discussed benchmarks for machine learning modeling approaches to materials and manufacturing research and development.
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He also teaches the rubric in a machine learning course for engineers at Georgia Tech. That is how they are teaching people to walk through machine learning problems.
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Ten years ago, Stebner said, machine learning was fun and exciting to apply to materials science problems, and many papers were published showing how well it worked in various situations. But, he added, "I think we're near the point where we need to say, okay, what am I getting from using machine learning that I couldn't get other ways?
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One of the major questions facing the development of this facility is how to automate everything upstream of the machine learning model training. "There's a full hour talk I have on what it takes to automate all of these, from the data management and curation, getting data into the form you need, and identifying which features are best for the statistical modeling," he said.
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He began by talking about coatings, noting that this is his area of expertise. Coatings have various benefits for use in extreme environments.
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The best-performing coatings now are the ultrahard diamond, and cubic or wurtzite boron nitride, which can resist pressures of more than 80 gigapascals, and super-hard transition metal carbides, nitrides, and borides, which can resist more than 40 gigapascals. Speaking of the challenges related to ultrahard materials he spoke first of be ing able to synthesize new ultrahard materials and dealing with the impurities that result in grain-boundary melting and affect the material's mechanical properties.
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From page 37... ...
Opila, University of Virginia, the final panelist, spoke about the thermochemical stability of materials in extreme environments, with a focus on degradation mechanisms. The context for the talk was the testing materials for use in extreme environments, such as protective materials on a space shuttle, and what can be learned from how those materials fail.
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From page 38... ...
Testing materials for use in hypersonic vehicles requires a different approach because the temperatures get much higher and cannot be achieved with the usual laboratory furnace. Instead, she said, the team uses resistive heating of the sample in a vacuum chamber where the oxidizing gases can be controlled.
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From page 39... ...
The lack of kinetic data is also a challenge, especially in complex materials. Opila ended with a set of research questions: • Do we have sufficient understanding of extreme environments?
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• Aaron Stebner: He described a nine-point rubric for success in using machine learning for engineering. It is important to note where subject matter expert input is needed; machine learning is not going to make that knowledge and experience redundant.
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From page 41... ...
That is something that is common to pretty much every scientific field, he said, and in many cases the first part of doing machine learning work is conditioning the data. Access to well-curated data sets is the key to using machine learning in materials science, and the key feature of a data set may not be whether it is large or small -- both types have their applications -- but rather the quality of the data set and how well curated it is.
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Thermo-Mechanical Testing and Characterization in Extreme Environments Gregory B Thompson, University of Alabama, began by speaking about the importance of characterizing materials in extreme environments, such as at extreme temperatures, under high strains, or under irradiation.
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The first is that it uses small sample sizes, which is advantageous for many of the materials designed for extreme environments, as they can be very expensive to produce. The second is that the technique has the potential to study a reasonably broad range of materials.
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From page 44... ...
, pp. 185-196 Three pairs of orthogonal electrodes stabilize the sample sphere via electrostatic levitation 200 W YAG laser beam heats and rotate the sample at many thousands of revs/s (absorbed photons apply small net torque)
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It is also possible to test materials at extreme temperatures and extreme me chanical properties at the same time, Thompson said, describing as an example a tool developed at Sandia National Laboratory. The tool is based on a transmission electron microscope (TEM)
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From page 46... ...
In summary, he said that in situ electron microscopy offer unique capabilities for probing phase stability, deformation mechanisms, and other properties, but developing measurement methods and specimen preparation is challenging. And, looking to the future, he noted that extreme environments offer a rich-realm of experimental opportunities in instrumentation development, measurement sci ence, and, ultimately, materials development.
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From page 47... ...
, the measurement of sample conditions during the experiment (direct measure leads to lower uncertainty than indirect measure) , the time that the sample is in the experimental conditions (longer times lead to smaller uncertainties)
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From page 48... ...
A collected data set is a repository where the burden of growth is on the community, there is minimal oversight of data quality, and missing data are okay. Calculated data come from density functional theory, machine learning, artificial intelligence, CALPHAD methods, or simulations.
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From page 49... ...
There are few measurement techniques or facilities for generating reference-quality data on materials in extreme conditions. There is also a lack of innovation in meth ods to measure these properties.
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Tabletop Hypervelocity Launcher for Studying Extreme Materials Following Rasmussen, Dana D Dlott, University of Illinois at Urbana-Cham paign, described how he studies materials under extreme conditions that he creates by using a pulsed laser to launch "little bullets" into the material.
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