Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Jia, Xiwen; Lynch, Allyson; Huang, Yuexiang; Danielson, Matthew L.; Lang'at, Immaculate; Milder, Alexander; Ruby, Aaron E; Wang, Hao; Friedler, Sorelle A.; Norquist, Alexander J.; Schrier, Joshua

doi:10.1038/s41586-019-1540-5

Cited by 191 publications

(200 citation statements)

References 35 publications

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“…However, many existing experimental data repositories are still either too small or too heterogeneous (e.g., different experimental conditions or measurement techniques) for high quality ML models. Human bias can also affect data diversity . Furthermore, the vast majority of databases are commercial products requiring a license, and programmatic application programming interfaces (APIs) for large‐scale data access are rarely implemented.…”

Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Data Collectionmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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“…An outstanding question with regard to the next generation of experimental workows is how to best combat human biases that can severely limit innovation. 77 Advanced autonomous experimentation may remove biases within a given search space through computationally designed experiments. However, the scope of the search space is limited by both instrument capabilities and active learning strategy, whose designs originate with human identication of the materials space of interest.…”

Section: Discussionmentioning

confidence: 99%

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

2019

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“…17 The breadth of these materials development strategies far exceeds the breadth of candidate photoanodes for which they have been deployed. The general bias in scientific research towards continued investigation of well-researched materials is both broadly known and recently evaluated as a limitation on creativity and discovery, 18 21 and computational investigation of β-Cu 2 V 2 O 7 22 to elucidate performance-limiting properties. Of these, only the copper vanadates exhibit a photon energy onset of photoactivity near 2 eV, the desired upper-limit described above.…”

Section: Toc Graphicsmentioning

confidence: 99%

Successes and Opportunities for Discovery of Metal Oxide Photoanodes for Solar Fuels Generators

et al. 2020

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The importance of metal oxide photoanodes in solar fuels technology has garnered concerted efforts in photoanode discovery in recent decades, which complement parallel efforts in development of analytical techniques and optimization strategies using standard photoanodes such as TiO2, Fe2O3 and BiVO4. Theoretical guidance of high-throughput experiments has been particularly effective in dramatically increasing the portfolio of metal oxide photoanodes, motivating a new era of photoanode development where the characterization and optimization techniques developed on traditional materials are applied to nascent photoanodes that exhibit visible light photoresponse. The compendium of metal oxide photoanodes presented in the present work can also serve as the basis for further technique development, with a primary goal to establish workflows for discovery of materials that perform better against the critical criteria of operational stability, visible light photoresponse, and photovoltage suitable for tandem absorber architectures.

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Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Cited by 191 publications

References 35 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

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

Successes and Opportunities for Discovery of Metal Oxide Photoanodes for Solar Fuels Generators

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