Evidence-based recommender system for high-entropy alloys

Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster–Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe–Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

Evidence-based recommender system and experimental validation for high-entropy alloys

We present a data-driven approach to explore high-entropy alloys (HEAs). To overcome the challenges with numerous element-combination candidates, selecting appropriate descriptors, and the limitations and biased of existing data, we apply the evidence theory to develop a descriptor-free evidence-based recommender system (ERS) for recommending HEAs. The proposed system measures the similarities between element combinations and utilizes it to recommend potential HEAs. To evaluate the ERS, we compare its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known data sets, including binary and ternary alloys. The results of experiments using k-fold cross-validation on the data sets show that the ERS outperforms all competitors. Furthermore, the ERS shows excellent extrapolation capabilities in experiments of recommending quaternary and quinary HEAs. We experimentally validate the most strongly recommended Fe-Co-based magnetic HEA, viz. FeCoMnNi, and confirm that it shows a body-centered cubic structure and is stable at high temperatures.

A biological source of marine sedimentary iron oxides

The biogeochemical cycle of iron is intricately linked to numerous element cycles. Although reductive biological processes that bridge the iron cycle to other element cycles are established, little is known about microbial oxidative processes on iron cycling in sedimentary environments—resulting in the formation of iron oxides. Here, we show that a major source of sedimentary iron oxides originates from the metabolic activity of iron-oxidizing bacteria from the class Zetaproteobacteria, stimulated by burrowing animals in coastal sediments. Zetaproteobacteria were estimated to be a global total of 1026 cells in coastal, bioturbated sediments and would equate to an annual production of approximately 7.9 x 1015 grams of sedimentary iron oxides—twenty-five times larger than the annual flux of iron oxides by rivers. These data suggest that iron-oxidizing Zetaproteobacteria are keystone organisms in marine sedimentary environments given their low numerical abundance; yet exert a profound impact via the production of iron oxides.

Analysis of completed commercial semiconductors using EPMA

The development of specialized analytical instruments such as the SIMS, XPS, ISS etc., all with truly incredible abilities in certain areas, has given rise to the notion that electron probe microanalysis (EPMA) is an old fashioned and rather inadequate technique, and one that is of little or no use in such high technology fields as the semiconductor industry. Whilst it is true that the microprobe does not possess parts-per-billion sensitivity (ppb) or monolayer depth resolution it is also true that many times these extremes of performance are not essential and that a few tens of parts-per-million (ppm) and a few tens of nanometers depth resolution is all that is required. In fact, the microprobe may well be the second choice method for a wide range of analytical problems and even the method of choice for a few.The literature is replete with remarks that suggest the writer is confusing an SEM-EDXS combination with an instrument such as the Cameca SX-50. Even where this confusion does not exist, the literature discusses microprobe detection limits that are seldom stated to be as low as 100 ppm, whereas there are numerous element combinations for which 10-20 ppm is routinely attainable.