A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses

When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel site environment features that characterize interstice distributions around atoms combined with machine learning (ML) can reliably identify plastic sites in several Cu-Zr compositions. Using only quenched structural information as input, the ML-based plastic probability estimates ("quench-in softness" metric) can identify plastic sites that could activate at high strains, losing predictive power only upon the formation of shear bands. Moreover, we reveal that a quench-in softness model trained on a single composition and quenching rate substantially improves upon previous models in generalizing to different compositions and completely different MG systems (Ni62Nb38, Al90Sm10 and Fe80P20). Our work presents a general, data-centric framework that could potentially be used to address the structural origin of any site-specific property in MGs

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials.

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ- or X-point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, L-, or W-point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum. We do this by constructing an "orbital phase diagram" to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy

Toward Self-Reliant Wind Farms

Large-scale integration of renewable energy generators, inverter-based resources and network interconnections into the grid brings forth a massive penetration of power electronic converters. This results in a highly dynamic environment that poses a risk to stability of system voltage and frequency and can ultimately trigger wide-area blackouts. Since conventional synchronous generation is being phased out, alternate sources must be included to provide support through ancillary services in future power networks. In a completely decarbonized system, they must also take the lead in ensuring stability and security by participating in blackout defense and network restoration. Offshore wind power plants are deemed suitable candidates due to their capability of providing large amounts of power with fast startup times and advanced control functionalities. However, a change in control philosophy to grid forming is required to enable a more active participation from the next-generation wind turbines. Such changes also have the potential to minimize dependence on auxiliary diesel gensets for a greener carbon footprint. This chapter aims to give insight into the forthcoming challenges and highlight potential solutions to make wind farms more self-reliant resulting in wind energy as cornerstone of the future electricity supply

Synthesis and Processing of Nanocrystalline Zirconium Carbide Formed by Carbothermal Reduction

Zirconium carbide (ZrC) powders were produced by carbothermal reduction reactions using fine-scale carbon/metal oxide mixtures as the starting materials. The reactant mixtures were prepared by pyrolytic decomposition of solution-derived precursors. The latter precursors were synthesized via hydrolysis/condensation of metal-organic compounds. The first step in the solution process involved refluxing zirconium alkoxide with 2,4 pentanedione ("acacH") in order to partially or fully convert the zirconium alkoxy groups to a chelated zirconium diketonate structure ("zirconium acac"). This was followed by the addition of water (under acidic conditions) in order to promote hydrolysis/condensation reactions. Precursors with variable carbon/metal ratios were produced by varying the concentrations of the solution reactants (i.e., the zirconium alkoxide, "acacH," water, and acid concentrations.) It was necessary to add a secondary soluble carbon source (i.e., phenolic resin or glycerol) during solution processing in order to obtain a C/Zr molar ratio close to 3 (as required for stoichiometry) in the pyrolyzed powders. The phase development during carbothermal reduction was investigated for oxide-rich carbon-deficient and slightly carbon-rich compositions. The reaction was substantially completed after heat treatments in the range of ~1400-1500oC. The crystallite sizes were in the range of ~100-130 nm. However, some oxygen dissolved in the lattice and some free carbon was present. Heat treatment at temperatures >1600oC was required to complete the reaction. The dry-pressed powder compacts, with varying C/Zr molar ratios, were pressureless sintered to relative densities in the range of ~98-100% at 1950oC.M.S.Committee Chair: Dr. Michael Sacks; Committee Member: Dr. Joe Cochran; Committee Member: Dr. Robert Speye

Fair GANs through model rebalancing with synthetic data

Deep generative models require large amounts of training data. This often poses a problem as the collection of datasets can be expensive and difficult, in particular datasets that are representative of the appropriate underlying distribution (e.g. demographic). This introduces biases in datasets which are further propagated in the models. We present an approach to mitigate biases in an existing generative adversarial network by rebalancing the model distribution. We do so by generating balanced data from an existing unbalanced deep generative model using latent space exploration and using this data to train a balanced generative model. Further, we propose a bias mitigation loss function that shows improvements in the fairness metric even when trained with unbalanced datasets. We show results for the Stylegan2 models while training on the FFHQ dataset for racial fairness and see that the proposed approach improves on the fairness metric by almost 5 times, whilst maintaining image quality. We further validate our approach by applying it to an imbalanced Cifar-10 dataset. Lastly, we argue that the traditionally used image quality metrics such as Frechet inception distance (FID) are unsuitable for bias mitigation problems

From the computer to the laboratory: materials discovery and design using first-principles calculations

The development of new technological materials has historically been a difficult and time-consuming task. The traditional role of computation in materials design has been to better understand existing materials. However, an emerging paradigm for accelerated materials discovery is to design new compounds in silico using first-principles calculations, and then perform experiments on the computationally designed candidates. In this paper, we provide a review of ab initio computational materials design, focusing on instances in which a computational approach has been successfully applied to propose new materials of technological interest in the laboratory. Our examples include applications in renewable energy, electronic, magnetic and multiferroic materials, and catalysis, demonstrating that computationally guided materials design is a broadly applicable technique. We then discuss some of the common features and limitations of successful theoretical predictions across fields, examining the different ways in which first-principles calculations can guide the final experimental result. Finally, we present a future outlook in which we expect that new models of computational search, such as high-throughput studies, will play a greater role in guiding materials advancements

Predicting the Volumes of Crystals

New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.Comment: 8 figures, 2 table

The temperature-dependence of carrier mobility is not a reliable indicator of the dominant scattering mechanism

The temperature dependence of experimental charge carrier mobility is commonly used as a predictor of the dominant carrier scattering mechanism in semiconductors, particularly in thermoelectric applications. In this work, we critically evaluate whether this practice is well founded. A review of 47 state-of-the-art mobility calculations reveals no correlation between the major scattering mechanism and the temperature trend of mobility. Instead, we demonstrate that the phonon frequencies are the prevailing driving forces behind the temperature dependence and can cause it to vary between $T^{-1}$ to $T^{-3}$ even for an idealised material. To demonstrate this, we calculate the mobility of 23,000 materials and review their temperature dependence, including separating the contributions from deformation, polar, and impurity scattering mechanisms. We conclusively demonstrate that a temperature dependence of $T^{-1.5}$ is not a reliable indicator of deformation potential scattering. Our work highlights the potential pitfalls of predicting the major scattering type based on the experimental mobility temperature trend alone