Abstract
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.
Highlights
Background & SummaryThe availability of scientific database systems[1], fast measurement instruments[2] and network infrastructures enable scientists to assemble ultra large datasets that enable to go beyond the answering of some original research question and gain fundamentally new knowledge via learning on all data collected[3]
These methods are expanded versions of descriptions in our related work, which is referenced below for each technique. All samples in this dataset were synthesized via ink-jet printing of precursor salts with subsequent thermal processing to form metal oxides[17]
The general assumption is that any chosen metal precursor salt, e.g. Mn(NO3)[2], will thermally decompose under oxidizing conditions into a metal oxide, e.g. Mn oxide, via removal of the precursor’s anion as a gas, e.g. NO2
Summary
The availability of scientific database systems[1], fast measurement instruments[2] and network infrastructures enable scientists to assemble ultra large datasets that enable to go beyond the answering of some original research question and gain fundamentally new knowledge via learning on all data collected[3] Fields such as organic chemistry[4], drug design[5,6,7], ab-initio materials science[8], and biology gain rapid pace through the availability of large datasets that enable predictive machine learning models but experimental materials science lacks such ultra large datasets (with the notable exception of the High-throughput Experimental Materials Database - HTEM1) as different synthesis procedures, processing conditions and analyses effectively block the assembly due to prohibitive inconsistencies in the data across experimental runs. In this manuscript we will give some background about how the dataset was acquired and is structured
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