Development of an Adaptive Experimental Design Method Based on Probability of Achieving a Target Range through Parallel Experiments

Abstract

In materials design and development, experimenters must make experiments under various conditions until they achieve a required physical property, yield, cost, or other objective. Use of a regression model based on existing experimental results is one suitable way to reduce the number of experiments required and development costs. Although adaptive experimental design methods using regression models for sequential and parallel experiments have previously been developed, those methods are sequential sampling methods or maximization and minimization methods, which is not always suitable for material design. Therefore, we have developed an adaptive experimental design method for parallel experiments in the field of material design, which uses the probability that more than one experimental result will achieve a result within a target range of a property on the next set of parallel experiments. We used Gaussian process regression to consider correlation of the predicted values of a property in multiple experiments. The probability of achieving results within a required property range on the next set of parallel experiments is calculated on the basis of this correlation. Using five case studies, we demonstrated that the proposed method could select experimental conditions more efficiently than traditional methods without requiring any parameters to be set in advance.