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Machine learning for renewable energy materials

Gu, Geun Ho ; Noh, Juhwan ; Kim, Inkyung ; Jung, Yousung

Journal of materials chemistry. A, Materials for energy and sustainability, 2019, Vol.7 (29), p.1796-17117 [Periódico revisado por pares]

Cambridge: Royal Society of Chemistry

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  • Título:
    Machine learning for renewable energy materials
  • Autor: Gu, Geun Ho ; Noh, Juhwan ; Kim, Inkyung ; Jung, Yousung
  • Assuntos: Alternative energy ; Artificial intelligence ; Batteries ; Catalysis ; Climate change ; Energy ; Energy technology ; Global warming ; Innovations ; Learning algorithms ; Machine learning ; Photovoltaic cells ; Renewable energy ; Renewable resources ; Solar cells ; Solar energy ; Sustainability
  • É parte de: Journal of materials chemistry. A, Materials for energy and sustainability, 2019, Vol.7 (29), p.1796-17117
  • Descrição: Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. While the window of opportunity is closing, meeting these goals necessitates deploying new research concepts and strategies to accelerate materials discovery by an order of magnitude. Recent advancements in machine learning have provided the science and engineering community with a flexible and rapid prediction framework, showing a tremendous potential impact. Here we summarize the recent progress in machine learning approaches for developing renewable energy materials. We demonstrate applications of machine learning methods for theoretical approaches in key renewable energy technologies including catalysis, batteries, solar cells, and crystal discovery. We also analyze notable applications resulting in significant real discoveries and discuss critical gaps to further accelerate materials discovery. Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accelerate the discovery renewable energy materials.
  • Editor: Cambridge: Royal Society of Chemistry
  • Idioma: Inglês
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