In the paradigm of virtual high-throughput screening for materials, we have developed a semiautomated workflow or “recipe” that can help a material scientist to start from a raw data set of materials with their properties and descriptors, build predictive models, and draw insights into the governing mechanism. We demonstrate our recipe, which employs machine learning tools and statistical analysis, through application to a case study leading to identification of descriptors relevant to catalysts for CO2 electroreduction, starting from a published database of 298 catalyst alloys. At the heart of our methodology lies the Bootstrapped Projected Gradient Descent (BoPGD) algorithm, which has significant advantages over commonly used machine learning (ML) and statistical analysis (SA) tools such as the regression coefficient shrinkage-based method (LASSO) or artificial neural networks: (a) it selects descriptors with greater stability and transferability, with a goal to understand the chemical mechanism rather than fitting data, and (b) while being effective for smaller data sets such as in the test case, it employs clustering of descriptors to scale far more efficiently to large size of descriptor sets in terms of computational speed. In addition to identifying the descriptors that parametrize the d-band model of catalysts for CO2 reduction, we predict work function to be an essential and relevant descriptor. Based on this result, we propose a modification of the d-band model that includes the chemical effect of work function, and show that the resulting predictive model gives the binding energy of CO to catalyst fairly accurately. Since our scheme is general and particularly efficient in reducing a set of large number of descriptors to a minimal one, we expect it to be a versatile tool in obtaining chemical insights into complex phenomena and development of predictive models for design of materials.

First-principles density functional theory (DFT) calculations were used to determine decohesion properties of Σ5(012) grain boundary of Ni with dopants B, C, S, Cr, and Hf. The relative stability of sites was evaluated and cleavage energies were calculated. Electronic structure was used to understand these properties in terms of changes in bonding with addition of dopants. It was found that strengthening of the Ni grain boundary results from Hf, B, and Cr doping. In contrast, the grain boundary weakens with S and C doping. These results should be useful in the design of next-generation nanostructured Ni-based alloys with improved mechanical behavior.

Cosmic strings moving through matter produce wakes where the density is higher than the background density. We investigate the effects of such wakes occurring at the time of a first order quark-hadron transition in the early universe and show that they can lead to a separation of the quark-gluon plasma phase in the wake region, while the region outside the wake converts to the hadronic phase. Moving interfaces then trap large baryon densities in sheetlike regions which can extend across the entire horizon. A typical separation between such sheets, at formation, is of the order of 1 km. Regions of baryon inhomogeneity of this nature, i.e., having a planar geometry and separated by such large distance scales, appear to be well suited for recent models of inhomogeneous nucleosynthesis to reconcile the large baryon to photon ratio implied by the recent measurements of the cosmic microwave background power spectrum.

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