Frontiers in Data Analytics and Monitoring Tools for Extreme Materials Proceedings of a Workshop (2023) / Chapter Skim
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From page 2... ...
The first set comprised various sorts of microscopes and other monitoring tools that can be used to characterize these materials, while the second consists of new data analytics tools, such as machine learning capabilities. By combining the ob servational tools with data analytics tools, it is possible to characterize the behavior of materials in extreme environments much more quickly and in greater details than has been possible in the past.
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Finding Thermally Robust Superhard Materials with Machine Learning Superhard materials have a large variety of uses, from drill bits to artificial joints, with estimated annual sales expected to reach around $14 billion by 2024, and as Jakoah Brgoch, University of Houston, told the workshop, the coming transition to clean energy will make it even more important to find superhard materials for use in extraction of the minerals needed for clean energy technologies. Thus, Brgoch's group has been looking for new superhard materials that can be produced efficiently and used under demanding conditions, such as high temperatures.
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By contrast, diamond, cubic boron nitride, and BC2N have a Vickers hardness well about 40 gigapascals no matter what load is put on them. A second issue with the use of the second group of materials for superhard applications, he explained, is that many of the transition metals are very scarce, so it would not be feasible to make commercial amounts of a superhard material that relied on one of very rare transition metal elements.
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From page 5... ...
This would be convenient, as the bulk modulus and shear modulus of materials can be calculated relatively easily with density-functional theory, a computational method based on quantum mechanics. However, when his group tried this approach, they found that while they could get figures that were in the ballpark of Vickers hardness, they were not accurate enough for the group's purposes.
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The next step, Brgoch continued, was to settle on an algorithm that would find relationships between the set of variables and the Vickers hardness, which, if it was successful, would make it possible to accurately predict the Vickers hardness of materials for which the hardness had not been measured, just based to the other properties of the material. The team tried a variety of machine learning algorithms, such as random forest, gradient boosting, and XGBoost, and the XGBoost model had the best performance, with an R2 correlation of 0.97 between the predicted value of Vickers hardness and the measured value, indicating a very strong per formance (Figure 2-1)
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It turned out to have "an unbelievably complex crystal structure," Brgoch noted, with a unit cell that was around 1,000 angstroms across with a lot of disorder. In particular, he commented, that complexity would have made it impossible to calculate the material's Vickers hardness with model calculations, but when the team measured the material's hardness, they found that the measurement agreed almost perfectly with what the machine learning algorithm had predicted.
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Next, the team applied their machine learning algorithm to create a model that predicts temperature-dependent Vickers hardness. They found that the model's results were not as clean as the earlier results -- which made sense because it was based on far fewer data -- but it was still able to capture the basic trend of how Vick ers hardness varied with temperature, with the hardness dropping as the tempera ture increased.
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More generally, she continued, pressure can produce new phases and forms in materials, it can induce and tune desirable properties, and it can also be used to provide important insights into materials' behavior, which can in turn be useful in aiding the design and discovery of materials with exceptional properties that might be suitable for extreme environments. The first step that Mao's team takes in using high temperatures to create new materials is to "study systems of interest over a wide range of pressures and temperatures using a suite of in situ and ex situ probes." A typical way to do this, she explained, is to use diamond anvil cells, in which a sample is held between the flat faces of two diamonds, with a gasket preventing the sample from escaping as pressure is applied.
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As the pressure increases up to 40 gigapascals, the sphere is progressively squashed, becoming increasingly denser, and that increased density remains even when the pressure is removed. Measurements indicated that after being exposed to 40 gigapascals, the density of the glassy carbon was permanently increased by 30 percent.
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These can be thought of as strips of graphene, and because they have a tunable band gap, they have some very useful properties. However, it is difficult to make these graphene nanoribbons, especially long, thin ones with smooth edges, so a faculty member in the Stanford chemistry department suggested that it might be easier to create nanoribbons by first synthesizing carbon nanotubes and then flattening the tubes in a diamond anvil cell.
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They loaded a diamond anvil with glassy carbon and argon, which diffused into the nanopores of the glassy carbon. When this was all exposed to pressures of greater than 20 gigapascals, the glassy carbon was transformed into nanodiamond, which held high-pressure argon in its inclusions (Figure 2-2)
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"In Science," she said, "we had some model nanocrystal systems where they did interparticle sintering and found that's able to further maximize the kinetic barrier, and they found out those phases didn't go back, and now also they add some ligands." Machine Learning Tools for Extending Quantum Simulations of Reacting Materials Nir Goldman, Lawrence Livermore National Laboratory, spoke about using machine learning to fill in a gap between what quantum simulations can provide and what experimentalists would like to know in terms of synthesizing materials. In particular, he spoke about a specific model, the Chebyshev Interaction Model for Efficient Simulation (ChIMES)
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Furthermore, the development of additive manufacturing has added another layer of complexity because one needs to consider not only the chemistry of the materi als and how they interact but also their grain size and the geometry of the printed material. "All of these things, in particular under dynamic compression, can lead you to different chemical paths," he said, "So if you have a desired outcome and you want to do materials design and additive manufacturing, you have this gigantic problem to deal with." The traditional approach to solving such a problem would be trial and error: start with a molecule, synthesize it, create some sort of device with it, then measure it, and repeat over and over again until a suitable path is found to the desired mate rial.
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In the remainder of his talk, he described two types of machine learning approaches that his group has worked on. One is a classical molecular dynamics approach that involves no quantum mechanical calculations, while the other involves a mixture of quantum mechanical approaches and empirical functions.
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Switching then to the second machine learning approach that his team has developed, Goldman described how the group has used semi-empirical quantum mechanics to get closer to something that is closer to plug and play -- that is, much more ready for download and use. In particular, they use data from existing high-accuracy quantum chemical databases to improve semi-empirical quantum mechanical models via machine learning.
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It is derived directly from density functional theory and can be thought of as approximate density functional theory, but the quantum mechanical parts -- which represent the band structure and coulomb forces -- are "pre-tabulated" via approximate quantum mechanics, while the shortrange repulsive energy is represented by an empirically fit function. In this form, the solution is much easier for machine learning to determine.
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The contribution of the grain boundaries to the properties of the material is the more difficult issue to study because the crystal line structures of the individual grains are relatively simple, while grain boundaries are quite complex and variable, depending on such things as the size and shape of the grains and how the crystalline structures of two adjacent grains are aligned relative to each other. The Olmsted data set was constructed by computing the atomic arrangements of 388 different cases where two grains were next to each other at differing orienta tions relative to each other.
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"We're able to predict grain boundary energy, grain boundary mobility, and whether grain boundaries are shear coupled or not using this machine learning," he said. "And this is coming from when we only have 388 datapoints in this data set.
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2010. FIGURE 2-3 Grain boundary energy over the five-dimensional space of possible alignments.
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This exemplifies another benefit of the Olmsted database, Homer said: "It has reinvigorated the field in terms of studying grain boundary migration." More generally, he said, "It has moved our field forward in important ways. It has led to the development of new methods.
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From page 22... ...
Such materials would cover the tip and leading edges of these vehicles as well as propulsion system components, and they would need to have high melting temperatures -- preferably above 3,000°C -- as well as mechanical strength, oxidation resistance, good thermal conductivity, and thermal shock resistance. The materials currently used for such purposes are reinforced carbon–carbon composites.
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From page 23... ...
method, which allows one to determine the boundaries between phases if the Gibbs free energies of the phases are known; a weakness of this approach is that it works only for known phases and cannot predict the existence of others. Another approach is DFT, which allows one to search for and explore different crystalline structures but is very computation-intensive and thus expensive for complex calculations.
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" Switching to the question of how to predict hardness in materials, Weinberger referred to Jakoah Brgoch's presentation earlier in the day on using machine learn ing to find thermally robust superhard materials and then offered some additional thoughts. A common approach to predicting material hardness, he said, is to use elastic constants, but there are cases where hardness does not behave as the elastic constants would suggest.
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The issue of creep is an even bigger problem, and he concluded by stating "We do not have good mesoscale models of creep. It's a very tough problem, and we haven't as a community invested in that enough.… But as a modeler, that's a great place to put effort so that we can start to get back the experiments on creep." Fundamental Mechanisms Under Extreme Environments and the Role of Machine Learning Penghui Cao, University of California, Irvine, spoke about the role that machine learning can play in studying fundamental mechanisms in extreme environ
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. Explaining why random solid solutions would be expected to form in these multi-principle-element materials, Cao said that when an alloy is formed at tem peratures close to the melting points of the component elements, the Gibbs free energy -- which is the sum of mixing enthalpy and configurational entropy -- would be dominated by the entropy, resulting in a random solid solution in which the component elements are randomly distributed, as in an intermetallic compound.
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The calculated values of f for that alloy at 3,000 K, 800 K, and 400 K were 0.57, 0.034, Diffusion and atomic jump processes Smooth energy landscape Diffusivity Energy vacancy vacancy � Vacancy atom ∆𝐺𝐺𝐺 atom � � Coordinate 𝒇𝒇 ⟶ diffusion correlation factor ∆𝑮𝑮𝑮⟶ diffusion energy barrier Rugged diffusion energy landscape FIGURE 2-4 Diffusion and atomic jump processes.
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