Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides

Kaneko, M.; Fujii, Minoru; Hisatomi, Takashi; Yamashita, Koichi; Domen, Kazunari

doi:10.1016/j.jechem.2019.01.012

Cited by 26 publications

(17 citation statements)

References 50 publications

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“…6c). The conclusion indicates that the most stable perovskite BaNbO 2 N supercells had each Nb atom coordinated with two N atoms, along with NbN chains in a cis conformation 98 . This work has suggested an approach for the property predictions of complex-compositions materials at a reasonable computational cost and provided guidance for the design of stable perovskite oxynitrides.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 96%

“…Reproduced with permission from ref. 98 . Copyright Elsevier 2019 d The predicted phase-transition energy difference ΔE versus DFT calculations.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

“…Kaneko et al 98 proposed a regression model built by ML based on the data with DFT calculations to predict the stability of anion ordering in perovskite-type BaNbO 2 N supercells. DFT was used to calculate the total energies of 560 small BaNbO 2 N supercells with random anion ordering.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning for perovskite materials design and discovery

et al. 2021

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The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

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Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 96%

“…Reproduced with permission from ref. 98 . Copyright Elsevier 2019 d The predicted phase-transition energy difference ΔE versus DFT calculations.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

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Machine learning for perovskite materials design and discovery

et al. 2021

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“…Particularly, above 10% quantum yield has been accomplished with TaON and Ta 3 N 5 photocatalysts [69][70][71]. Domen and co-workers mostly investigated oxynitride based photoelectrodes as an alternative to oxide material, which is likely to arrest the incoming photons for water oxidation reactions [72][73][74]. In this regard, creating highly active oxynitride-based photoanodes is significant in achieving a high-efficiency PEC water photoelectrolysis process.…”

Section: (Oxy)nitrides Based Materialsmentioning

confidence: 99%

Recent Developments in the Use of Heterogeneous Semiconductor Photocatalyst Based Materials for a Visible-Light-Induced Water-Splitting System—A Brief Review

et al. 2021

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Visible-light-driven photoelectrochemical (PEC) and photocatalytic water splitting systems featuring heterogeneous semiconductor photocatalysts (oxynitrides, oxysulfides, organophotocatalysts) signify an environmentally friendly and promising approach for the manufacturing of renewable hydrogen fuel. Semiconducting electrode materials as the main constituents in the PEC water splitting system have substantial effects on the device’s solar-to-hydrogen (STH) conversion efficiency. Given the complication of the photocatalysis and photoelectrolysis methods, it is indispensable to include the different electrocatalytic materials for advancing visible-light-driven water splitting, considered a difficult challenge. Heterogeneous semiconductor-based materials with narrower bandgaps (2.5 to 1.9 eV), equivalent to the theoretical STH efficiencies ranging from 9.3% to 20.9%, are recognized as new types of photoabsorbents to engage as photoelectrodes for PEC water oxidation and have fascinated much consideration. Herein, we spotlight mainly on heterogenous semiconductor-based photoanode materials for PEC water splitting. Different heterogeneous photocatalysts based materials are emphasized in different groups, such as oxynitrides, oxysulfides, and organic solids. Lastly, the design approach and future developments regarding heterogeneous photocatalysts oxide electrodes for PEC applications and photocatalytic applications are also discussed.

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“…They can only rely on manual classification and summary to screen materials with required characteristics, which consumes a lot of labor and time. Applying AI approaches increase the efficiency via modeling and optimization without increasing the cost (Zalesny, 2017;Bin Janai et al, 2018;Kaneko et al, 2019). Hence, AI technology plays a significant role in energy conversion and other fields.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

et al. 2020

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With the characteristics of high-speed calculation and high-accuracy prediction, artificial intelligence (AI) which also known as machine intelligence, including deep learning, machine learning, etc., have shown great advantages in cross-field applications. In material science field, AI can be used to discover new materials and predict corresponding critical properties. At present, AI has been used in the exploitation of energy conversion materials and other energy-related materials. In this review, we summary the current achievements of AI applications in energy conversions, analyze the advantages and disadvantages of AI techniques in material researches and point out future development prospects.

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Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides

Cited by 26 publications

References 50 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

Recent Developments in the Use of Heterogeneous Semiconductor Photocatalyst Based Materials for a Visible-Light-Induced Water-Splitting System—A Brief Review

Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials

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