We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form part of a data analysis system that integrates data mining, materials databases, and measurement tools, to provide high throughput analysis of materials data. Of primary interest is the high throughput analysis of experimental data measured on combinatorial "libraries", both on-the-fly (real-time) during the measurement experiment, as well as offline, i.e., after data collection. The advantage of the former is the possibility of providing real-time guidance during measurement, to improve data collection.
Over the last few decades, materials discovery and optimization have become significantly more sophisticated through the use of high throughput (combinatorial) methodologies. As a result, materials researchers can now collect much more data than previously possible, resulting in larger data sets that can be very time consuming to analyze. The disparity between data collection and analysis times is fueling interest in new machine learning algorithms, also known as data-mining techniques. These techniques are especially needed when the individual data takes on non-trivial, high dimensional forms such as spectra or images.
Combinatorial Library Data:
Integration with Instrumentation and Real-Time Analysis: