"Is Predictive Materials Degradation Within Reach?"
Limited stability and unacceptable degradation products are common reasons for otherwise promising materials to fail technological translation. The enduring state-of-the-art for establishing these properties essentially remains make-and-break testing, which is costly and provides information only at the end of the materials development process. Recent developments in automated reaction prediction potentially provide the means of reversing this paradigm so that stability properties and degradation pathways can be designed like other functional material properties. In this talk I will highlight our group’s recent work developing methods for predicting reaction outcomes and how they have been applied to several different materials classes. The second half of the talk will discuss machine learning approaches to the closely related problem of identifying degradation products on the basis of typical spectral information sources.
