The binary transition-metal monophosphides CrP, MnP, FeP, and CoP have been studied with X-ray photoelectron spectroscopy. The shifts in phosphorus 2p(3/2) core line binding energies relative to that of elemental phosphorus indicated that the degree of ionicity of the metal-phosphorus bond decreases on progressing from CrP to CoP. The metal 2p(3/2) core line binding energies differ only slightly and show similar line shapes to those of the elemental metals, reaffirming the notion that these transition-metal phosphides have considerable metallic character. The satellite structure observed in the Co 2p(3/2) X-ray photoelectron spectra of Co metal and CoP was examined by reflection electron energy loss spectroscopy and has been attributed to plasmon loss, not final state effects as has been previously suggested. Valence-band spectra of the transition-metal phosphides agree well with the density of states profiles determined from band structure calculations. The electron populations of the different electronic states were extracted from the fitted valence-band spectra, and these confirm the presence of strong M-P and weak P-P bonding interactions. Atomic charges determined from the P 2p core line spectra and the fitted valence-band spectra support the approximate formulation M(1+)P(1-) for these phosphides.
A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400,000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB 2 C and predict the existence of 12 novel gallides MRu 2 Ga and RuM 2 Ga (M = Ti-Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu 2 Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.
Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions.
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