Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence
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Abstract
Abstract We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition, with the voltage spectroscopy data being used as a proxy for the (unknown) microscopic state or thermodynamic parameters of materials. Given the discrete nature of the measurement points, we use Gaussian processes to reconstruct hysteresis loops in temperature and voltage space, and compare the results with the raw data and bulk dielectric spectroscopy measurements. The results indicate that the surface transition temperature is similar for all but one composition with respect to the bulk. Through clustering algorithms, we recreate the main features of the bulk diagram, and provide statistical confidence estimates for the reconstructed phase transition temperatures. We validate the method by using Gaussian processes to predict hysteresis loops for a given temperature for a composition unseen by the algorithm, and compare with measurements. These techniques can be used to map phase diagrams from functional materials in an automated fashion, and provide a method for uncertainty quantification and model selection.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Neural Networks and Applications · Statistical and numerical algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
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