Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Abstract

Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high‐throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high‐throughput techniques. For intuitive visualization, a graphical phase–property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning‐based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.

High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications

“…[7,8,10,62] Therefore, significant efforts have been dedicated to developing combinatorial synthesis approaches (aided by artificial intelligence) and theoretical predictions to hunt specific materials of interest for industry and academic research. [63,[64][65][66][67][68][69] A synergy among experimental, theoretical predictions, and artificial intelligence will significantly aid the development of multicomponent materials for targeted properties. A common trend in the workflows [64,67,[69][70][71][72] presented in different fields suggests a path toward developing autonomous and automated protocols, as depicted in Figure 5.…”

“…[63,[64][65][66][67][68][69] A synergy among experimental, theoretical predictions, and artificial intelligence will significantly aid the development of multicomponent materials for targeted properties. A common trend in the workflows [64,67,[69][70][71][72] presented in different fields suggests a path toward developing autonomous and automated protocols, as depicted in Figure 5. The workflow starts with combinatorial synthesis methods, automated characterization techniques (with or without a feedback-measuring loop), automated data analysis, creating a material database or material library, and selecting materials of interest.…”

“…For instance, following such an approach, it was possible to screen a vast compositional space for an HEO and present a material library relating chemical composition, crystal structure, and the bandgap. [69] Thus, research experience for element choice and theoretical predictions combined with high-throughput and artificial intelligence will accelerate the discovery of promising materials.…”

“…[65,67,72,74,75] In the case of HEOs (systems containing at least five cations), there is, up to date, one high-throughput study reported in the literature. [69] Therefore, in the following sections, the literature review is limited to some exemplary studies (conducted on ternary/ quaternary systems, organic-inorganic, and organic materials) that can be adapted to develop high-throughput studies on HEOs.…”

“…hundreds of different solutions containing different volumes to vary the chemical composition of the desired material or to produce combinatorial studies of several candidate elements. [69] As described in the organic-inorganic perovskites study by Li et al, [68] the resulting mixed solutions were not exposed to temperatures exceeding 105 °C, which are insufficient for the synthesis calcination process of HEOs (>700 °C) using wet chemistry. Therefore, such high-throughput synthesis protocols for the synthesis of HEOs require technical modifications.…”