There is intense interest in uncovering design rules that govern the formation of various structural phases as a function of chemical composition in multi-principal element alloys (MPEAs). In this paper, we develop a machine learning (ML) approach built on the foundations of ensemble learning, post hoc model interpretability of black-box models, and clustering analysis to establish a quantitative relationship between the chemical composition and experimentally observed phases of MPEAs. The originality of our work stems from performing instance-level (or local) variable attribution analysis of ML predictions based on the breakdown method, and then identifying similar instances based on k-means clustering analysis of the breakdown results. We also complement the breakdown analysis with Ceteris Paribus profiles that showcase how the model response changes as a function of a single variable, when the values of all other variables are fixed. Results from local model interpretability analysis uncover key insights into variables that govern the formation of each phase. Our developed approach is generic, model-agnostic, and valuable to explain the insights learned by the black-box models. An interactive web application is developed to facilitate model sharing and accelerate the design of MPEAs with targeted properties.
Growth of high quality two-dimensional transition metal dichalcogenide monolayers with the desired microstructure and morphology is critical for enabling key technological solutions. This is a non-trivial problem because the processing space is vast and lack of a priori guidelines impedes rapid progress. A machine learning approach is discussed that leverages the data present in published growth experiments to predict growth performance in regions of unexplored parameter space. Starting from the literature data on MoS2 thin films grown using chemical vapor deposition (CVD), a database is manually constructed. Unsupervised and supervised machine learning methods are used to learn from the compiled data by extracting trends that underlie the formation of MoS2 monolayers. Design rules are uncovered that establish the phase boundaries classifying monolayers from other possible outcomes, which offers future guidance of CVD experiments.
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