Colorimetric screening for high-throughput discovery of light absorbers

The Materials Provenance Store

We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

Bi Alloying into Rare Earth Double Perovskites Enhances Synthesizability and Visible Light Absorption

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm₂MnNiO₆ (double perovskite structure), of the form (Bi,Sm)₂MnNiO₆. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides

Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties.

Design Type(s)	classification objective • observation design
Measurement Type(s)	absorption spectrum
Technology Type(s)	ultraviolet-visible spectrophotometry
Factor Type(s)	metal oxide
Sample Characteristic(s)

Machine-accessible metadata file describing the reported data (ISA-Tab format)

Machine learning of optical properties of materials - predicting spectra from images and images from spectra

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 unique quinary oxide and 67 000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 180 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena yielding the type of complex data relationships that merit and benefit from neural network-type modelling.