Similarity of materials and data-quality assessment by fingerprinting

TL;DR

This paper presents a spectral fingerprinting method for quantifying material similarities and assessing data quality, enabling identification of similar materials, uncertainty evaluation, and exploration of material spaces.

Contribution

It introduces a spectral fingerprint approach combined with similarity metrics and clustering for material comparison, data quality assessment, and trend discovery.

Findings

Quantifies differences in optical spectra and computational properties.

Assesses uncertainty from diverse data sources.

Explores material spaces to find patterns and trends.

Abstract

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 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.