The purpose of this scoping workshop held by the Alan Turing Institute is to bring together data scientists with materials scientists and engineers to elucidate existing and potential future opportunities in the area of data-centric materials science and engineering. Data scientists will present their latest methods and algorithms which may be applicable to materials imaging and design, and materials scientists and engineers will present their latest experiments, techniques, resulting data sets, and objectives.
About the Event
This event will be a two-day workshop with roughly 10 short talks per day with the following breakdown. It is expected that many people may be interested in attending only one of the two days, although all participants will be welcome on both days.
Day 1: Digitization of images.
Given the importance of microstructure in determining material properties, in order to take advantage of computational science and machine learning approaches in materials discovery and manufacturing we need to find ways to digitise microstructural information. In other words we need concise ways to capture the essence of a material’s microstructure so that we can relate it to the resulting materials properties. A salient theme of day one will be the pre-processing and digitization of images — including techniques for data handling, compression, feature extraction and dimension reduction.
Day 2: Optimising materials manufacturing and discovery via data-centric engineering.
Talks on the second day will have a distinctly more data-centric engineering focus, centered around materials informatics—i.e., techniques for post-processing and leveraging the value embedded in materials databases. Existing and novel approaches within the fields of uncertainty quantification, digital twinning, design of experiments and prediction will be covered. There is particular interest in Bayesian methods, as well as methods to combine known physical models and expert knowledge together with data driven approaches for extrapolation and predictive capabilities beyond just explanatory models.
May 15 2019
