Authors: Aditya Sundar
Source: Deep Blue (University of Michigan) (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modeling the Structural and Chemical Stability of Materials in Reactive Environments Using Multiscale Methods Structural materials such as advanced metallic alloys with surface passivation layers are essential components in several emerging energy technologies and infrastructure. For instance, refractory metal alloys are used as components in aerospace probes, heat exchangers and claddings in nuclear, solar technologies, etc. Spontaneously formed surface passivation layers are useful as corrosion-resistant coatings, permeation barriers, etc., due to their excellent electronic and chemical insulation against external stimuli in ambient atmospheric conditions. However, exposure to harsh reactive environments, such as halide-containing aqueous electrolytes, reactive molecules, and gaseous impurities, impacts the integrity of these materials, eventually causing performance failure. This dissertation investigates material performance and response in such reactive environments by combining multiscale modeling methods with data-driven screening approaches. Advances in this work are focused on two general themes: 1) modeling and simulation for the mechanistic understanding of material properties in reactive conditions and 2) machine learning (ML) guided material design with synergistic properties. In the first half of this thesis, multiscale models are developed to investigate the properties of passive alumina, which is widely used due to its insulating character (bandgap ∼9 eV, poor ionic mobility, and electrochemical stability). First, density functional theory (DFT)-based simulations are used to uncover the atomistic and electronic mechanisms responsible for chloride-induced localized corrosion of metals with Al2O3 passive layers in electrochemical conditions. Results demonstrate the increased likelihood of metal depassivation at defect sites in alumina layers, such as surface terminations of grain boundaries, consistent with experimental observations. Second, our results demonstrate the suitability of alumina polymorphs as hydrogen permeation barriers, which is critical to improve hydrogen retention in solid-state hydride neutron moderators for proposed applications in next-generation nuclear microreactors. Gaussian approximation potentials (GAP) generated using ab initio molecular dynamics (AIMD) datasets are used to compute the diffusivity of H+ in Al2O3 polymorphs, since H+ is found to be the most stable hydrogen interstitial species by DFT calculations. Results from molecular dynamics simulations using GAP show close agreements with experimental hydrogen migration barriers. Comparable H+ diffusivities in crystalline and amorphous alumina indicate a weak dependence on structural order. H+ diffusivity at 1000 K is calculated to be ∼1E−7-1E−6 cm2/s, which is 2-3 orders of magnitude lower than hydrogen diffusivity in metals. The second half of the thesis is focused on evaluating the mechanical and chemical properties of advanced metallic alloys in high-temperature applications. A hierarchical multiscale screening workflow is developed to enable the discovery of refractory alloys with high room-temperature ductility for easy mechanical processing and high-temperature surface oxidation for corrosion resistance. By combining DFT, ML and high-throughput computational thermodynamics, the workflow is used to screen over 10 million quaternary alloys from a 13-element composition space to identify promising alloys that show room-temperature ductility comparable to that of pure Group V elements and have large thermodynamic driving forces for Al2O3/Cr2O3 formation at operational temperatures over 900 ◦C. Finally, a model is proposed to quantify CO2-induced oxidation and carburization in alloys promising for applications in supercritical CO2-based energy conversion cycles. By combining empirical models, CALculation of PHAse Diagrams (CALPHAD) and finite-element-method-based simulations, Ni alloys are shown to be more resistant to CO2-induced carburization compared to Fe alloys under identical conditions. Multiscale models developed here can be employed to guide the development of transition metal alloys and oxide coatings, which form the structural backbone of several emerging technologies. Stability (learning theory) ; Computer science ; Biological system ; Biology ; Machine learning Material Properties and Failure Mechanisms