Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd10RuCd3, a Neutron Absorption Material

The search for a neutron absorption material was aided by materials informatics tools, composing the developed recommendation engines. A new member in the ternary RE10MCd3 series, Gd10RuCd3, was predicted and synthesized as an experimental validation. The recommendation engine is based on partial least squares – discriminant analysis (PLS-DA) crystallographic site processing to classify the site preference of elements in compounds crystallizing in the Y10RuCd3-type structure and upon projecting other elements onto Principal Component map, the titled compound emerged as the top candidate. The distinguishing feature of the developed recommendation engine is in the three explainable methods utilized in the predictive framework: unrestricted, conservative, and cluster methods. Predictions are visualized with convenient property projection tools. The prediction was validated with high-temperature synthesis. The structure was confirmed with single crystal and powder X-ray diffraction. The cold-water-quenched samples quenched from 800 °C have smaller unit cell volume than the samples quenched from 600 °C annealing temperature. Transport properties measurements show an unusually low thermal conductivity (5-7 W·m-1K-1) and the trend change indicating a possible structure transition between 600 and 800 °C. The unusual low thermal conductivity was predicted with machine-learning model that focuses on thermoelectric property prediction. DFT analysis reveals the presence of a 0D electride phenomenon. The neutron cross section and absorption based on the constituent elements put our material within the top 0.4% of neutron absorbers. The negative thermal expansion and high mass absorption suggest Gd10RuCd3 as an attractive neutron absorption material.