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.

ObjectiveFew methodological studies address the prioritization of clinical topics for the development of Clinical Practice Guidelines (CPGs). The aim of this study was to validate a methodology for Priority Determination of Topics (PDT) of CPGs.Methods and resultsFirstly, we developed an instrument for PDT with 41 criteria that were grouped under 10 domains, based on a comprehensive systematic search. Secondly, we performed a survey of stakeholders involved in CPGs development, and end users of guidelines, using the instrument. Thirdly, a pilot testing of the PDT procedure was performed in order to choose 10 guideline topics among 34 proposed projects; using a multi-criteria analysis approach, we validated a mechanism that followed five stages: determination of the composition of groups, item/domain scoring, weights determination, quality of the information used to support judgments, and finally, topic selection. Participants first scored the importance of each domain, after which four different weighting procedures were calculated (including the survey results). The process of weighting was determined by correlating the data between them. We also reported the quality of evidence used for PDT. Finally, we provided a qualitative analysis of the process. The main domains used to support judgement, having higher quality scores and weightings, were feasibility, disease burden, implementation and information needs. Other important domains such as user preferences, adverse events, potential for health promotion, social effects, and economic impact had lower relevance for clinicians. Criteria for prioritization were mainly judged through professional experience, while good quality information was only used in 15% of cases.ConclusionThe main advantages of the proposed methodology are supported by the use of a systematic approach to identify, score and weight guideline topics selection, limiting or exposing the influence of personal biases. However, the methodology was complex and included a number of quantitative and qualitative approaches reflecting the difficulties of the prioritization process.

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