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Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu₆Sn₅intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rankσ_SI,σ_IL, andσ_IL, respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσ_SIandσ_SLalter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME.

 

The utilization of metallic anodes holds promise for unlocking high gravimetric and volumetric energy densities and is pivotal to the adoption of ‘beyond Li’ battery chemistries. Much of the promise of magnesium batteries stems from claims regarding their lower predilection for dendrite growth. Whilst considerable effort has been invested in the design of novel electrolytes and cathodes, detailed studies of Mg plating are scarce. Using galvanostatic electrodeposition of metallic Mg from Grignard reagents in symmetric Mg–Mg cells, we establish a phase map characterized by disparate morphologies spanning the range from fractal aggregates of 2D nanoplatelets to highly anisotropic dendrites with singular growth fronts and nanowires entangled in the form of mats. The effects of electrolyte concentration, applied current density, and coordinating ligands have been explored. The study demonstrates a complex range of electrodeposited morphologies including canonical dendrites with shear moduli conducive to penetration through typical polymeric separators. We further demonstrate a strategy for mitigating Mg dendrite formation based on the addition of molecular Lewis bases that promote nanowire growth through selective surface coordination. 

Solid solutions of Mg 2 Si and Mg 2 Sn are promising thermoelectric materials owing to their high thermoelectric figures-of-merit and non-toxicity, but they may undergo phase separation under thermal cycling due to the presence of miscibility gaps, implying that the thermoelectric properties could be significantly degraded during thermoelectric device operation. Herein, this study investigates the strain-induced suppression of the miscibility gap in solid solutions of Mg 2 Si and Mg 2 Sn. Separately prepared Mg 2 Si and Mg 2 Sn powders were made into (Mg 2 Si) 0.7 (Mg 2 Sn) 0.3 mixtures using a high energy ball-milling method followed by spark plasma sintering. Afterwards, the phase evolution of the mixtures, depending on thermal annealing and mixing conditions, was studied experimentally and theoretically. Transmission electron microscopy and X-ray diffraction results show that, despite the presence of a miscibility gap in the pseudo-binary phase diagram, the initial mixture of Mg 2 Si and Mg 2 Sn evolved towards a solid solution state after annealing for 3 hours at 720 °C. Thermodynamic analysis as well as phase-field microstructure simulations show that the strain energy due to the coherent spinodal effect suppresses the chemical spinodal entirely and prevents phase separation. This strategy to suppress the miscibility gap induced by lattice strain through non-equilibrium processing can benefit the thermoelectric figure-of-merit by maximizing phonon alloy scattering. Furthermore, stable solid solutions by engineering phase diagrams have the potential to facilitate the reliable long term operation of thermoelectric generators under continuous thermal loads.