Gaussian Processes and Deep Learning for Experimental Data

Several experimental disciplines depend upon exploring large and high-dimensional parameter spaces to search and find new scientific discoveries. For example, in materials science, the wide variety of pathways for synthesis, processing, and environmental conditions that influence material properties give rise to particularly vast parameter spaces. In order to improve efficiency during materials discovery, one of the main strategies is to increase automation of the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. This presentation will showcase some of the advantages of using ML methods to search for materials configurations in large databases, e.g. using pattern recognition for polymeric films. We will also discuss algorithms for handling data from high-throughput experiments, such as Convolutional Neural Networks (CNN) and Gaussian process regression (GPR), with a focus on a DOE-funded software called “gpCAM” for autonomous data acquisition, which is based on GPR. This talk will give an introduction to the inner workings of the algorithms, how to use it, and will present a handful of examples.