Automated algorithms for band gap analysis from optical absorption spectra

Schwarting, Marcus; Siol, Sebastian; Talley, Kevin R.; Zakutayev, Andriy; Phillips, Caleb

doi:10.1016/j.md.2018.04.003

Cited by 19 publications

(22 citation statements)

References 34 publications

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“…Samples were synthesized using ink-jet printing of precursors salts, typically metal nitrates, that are subsequently annealed to form metal oxides.. We cannot assert the absence of a bias towards either larger or smaller bandgap materials in this dataset since there is no comparably large dataset with optical properties on mixed metal oxides to compare. Samples were synthesized using ink-jet printing of precursors salts, typically metal nitrates, that are subsequently annealed to form metal oxides 31. Optical absorption spectra were recorded using an on-the-fly scanning UV-vis dual-sphere spectrometer as described elsewhere 22.…”

Section: Methodsmentioning

confidence: 99%

“…30 The recently reported machine learning model by Oses et al 7 achieves an RMSE of 0.51 eV for computationallypredicted band gaps, which improves band gap prediction but not band gap measurement. Recently published algorithms have automated the extraction of band gap energy from an ultraviolet-visible (UV-vis) optical absorption spectrum, 30,31 leaving spectrum acquisition as the rate limiting step of band gap measurement. As a result, the prediction of absorption spectra from a higher throughput experimental technique, such as imaging with a consumer product, would be quite impactful.…”

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confidence: 99%

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

et al. 2019

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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 autogenerated 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. † Electronic supplementary information (ESI) available: Details of model structure, additional model results, and le containing all model weights. See

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Section: Methodsmentioning

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confidence: 99%

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

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“…Both approaches are commonly used in combinatorial materials science where accelerated synthesis techniques include cosputtering, 6 co-evaporation, 10 ink-jet printing, 38 combinatorial ball-milling, 39 high-throughput hydrothermal synthesis, 40,41 and bulk ceramic hot-pressing. 42 Similarly, the acceleration of the characterization of materials properties and evaluation of performance for a target functionality have been the focus of extensive methods development in the past two decades, with notable demonstrations including electrochemical testing, [43][44][45][46] X-ray diffraction, [47][48][49] processing, 9,50,51 optical spectroscopy, 52,53 electric properties, 65,66 shape memory, 13,54 and phase dynamics. 9 These advancements in experiment automation have undoubtedly led to discoveries that would not have been made in the same time frame using traditional techniques.…”

Section: Automation and Parallelizationmentioning

confidence: 99%

Progress and prospects for accelerating materials science with automated and autonomous workflows

2019

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“…This dataset 10 will enable materials scientists to continue developing algorithms that build upon recent advances including finding embeddings for materials composition 11,12 , predicting optical properties 9 from composition, linking experimental findings to theory databases 8,13 , and extracting band gap energy from UV-Vis spectra 14,15 .…”

Section: Background and Summarymentioning

confidence: 99%

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

et al. 2019

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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.

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Automated algorithms for band gap analysis from optical absorption spectra

Cited by 19 publications

References 34 publications

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

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

Progress and prospects for accelerating materials science with automated and autonomous workflows

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

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