We present a rapid microwave-assisted solid-state approach to prepare complex platinum-group metal oxides with the formula La2BaMO5 (M = Pd, Pt). While conventional furnace-based preparations of these compounds take several days and often require oxidizing conditions, the microwave-assisted pathway enables the target compounds to be obtained with high phase purity in about 20 min of reaction time in air without the multiple regrindings that are required of conventional solid-state synthesis. These complex oxides are stable in various atmospheres up to 1000 °C unlike the simple noble metal oxides, which are reduced even at room temperature. Density functional theory-based calculations have been employed to establish the stability of these complex oxides and to understand the electronic structure origins of the stability, most notably the influence of electropositive cations. It is shown that the presence of electropositive ions in the oxide crystal structure "softens" the oxygen anion and results in more covalent (Pd/Pt)-O interactions.

The average and local structure of the oxides Ba$_2$SiO$_4$, BaAl$_2$O$_2$, SrAl$_2$O$_4$, and Y$_2$SiO$_5$ are examined in order to evaluate crystal rigidity in light of recent studies suggesting that highly connected and rigid structures yield the best phosphor hosts. Simultaneous momentum-space refinements of synchrotron X-ray and neutron scattering yield accurate average crystal structures, with reliable atomic displacement parameters. The Debye temperature $\Theta_{\mathrm{D}}$, which has proven to be a useful proxy for structural rigidity, is extracted from the experimental atomic displacement parameters and compared with predictions from density functional theory calculations and experimental low-temperature heat capacity measurements. The role of  static disorder on the measured displacement parameters, and the resulting Debye temperatures are also analyzed using pair distribution function of total neutron  scattering, as refined over varying distance ranges of the pair distribution function. The interplay between optimal bonding in the structure, structural rigidity, and correlated motion in these structures is examined, and the different contributions are delineated.

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. We cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.