Similarity of materials and data-quality assessment by fingerprinting

Identifying similar materials (i.e., those sharing a certain property or feature) requires interoperable data of high quality. It also requires means to measure similarity. We demonstrate how a spectral fingerprint as a descriptor, combined with a similarity metric, can be used for establishing quantitative relationships between materials data, thereby serving multiple purposes. This concerns, for instance, the identification of materials exhibiting electronic properties similar to a chosen one. The same approach can be used for assessing uncertainty in data that potentially come from different sources. Selected examples show how to quantify differences between measured optical spectra or the impact of methodology and computational parameters on calculated properties, like the density of states or excitonic spectra. Moreover, combining the same fingerprint with a clustering approach allows us to explore materials spaces in view of finding (un)expected trends or patterns. In all cases, we provide physical reasoning behind the findings of the automatized assessment of data. Impact statement To predict novel materials with desired properties, data-centric approaches are in the process of becoming an additional fundament of materials research. Prerequisite for their success are well-curated data. Ideally, one can make use of multiple data collections. Bringing data from different sources together, poses challenges on their interoperability which are routed in two out of the 4V of Big Data. These are the uncertainty of data quality (veracity) and the heterogeneity in form and meaning of the data (variety). To overcome this barrier, universal and interpretable measures must be established, which quantify differences between data that are supposed to have the same meaning. Here, we show how a spectral fingerprint in combination with a similarity metric can be used for assessing spectral properties of materials. Our approach allows for tracing back in computed as well as measured data, differences stemming from various aspects. It thus paves the way for automatized data-quality assessment toward interoperability. Based on this, in turn, materials exhibiting similar features can be identified. Graphical abstract