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.

CORRIGENDUMCorrigendum: Ensemble learning reveals dissimilarity between rareearth transition metal binary alloys with respect to the Curie temperature (2019 J. Phys. Mater. 2 034009) AbstractWe propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture models. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature (T C ) of binary 3d transition metal-4f rare-earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

The Curie temperature (T C) of RT binary compounds consisting of 3d transition-metal (T ) and 4f rare-earth elements (R) is analyzed systematically by a developed machine learning technique called kernel regression-based model evaluation. Twenty-one descriptive variables were designed assuming completely obtained information of the T C. Multiple kernel regression analyses with different kernel types: cosine, linear, Gaussian, polynomial, and Laplacian kernels were implemented and examined. All possible descriptive variable combinations were generated to construct the corresponding prediction models. As a result, by appropriate combinations between descriptive variable sets and kernel formulations, we demonstrate that a number of kernel regression models can accurately reproduce the T C of the RT compounds. The relevance of descriptive variables for predicting T C are systematically investigated. The results indicate that the rare-earth concentration is the most relevant variable in the T C phenomenon. We demonstrate that the regression-based model selection technique can be applied to learn the relationship between the descriptive variables and the actuation mechanism of the corresponding physical phenomenon, i.e., T C in the present case.

A machine-learning method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to discover the subgroup structure of materials, understand the underlying mechanisms, and support the prediction of the physical properties of materials.

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.