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Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery.

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Artificial intelligence accelerates perovskite solar cell (PSC) development by enabling data-driven additive discovery. AI optimizes formulations and elucidates mechanisms, paving the way for faster commercialization.

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Area of Science:

  • Materials Science
  • Renewable Energy
  • Artificial Intelligence

Background:

  • Additive engineering is vital for improving perovskite solar cell (PSC) performance.
  • Discovering effective additives is challenging due to the vast chemical space and traditional trial-and-error methods.

Purpose of the Study:

  • To present a paradigm shift towards AI-driven additive discovery for PSCs.
  • To explore the integration of AI in optimizing PSC formulations and understanding additive mechanisms.

Main Methods:

  • Establishing physicochemical foundations and machine learning descriptors for additive engineering.
  • Utilizing active learning algorithms for intelligent process optimization of precursor formulations.
  • Investigating AI for mechanism elucidation and employing generative models for additive discovery.

Main Results:

  • AI significantly reduces experimental iterations in tuning precursor formulations.
  • AI facilitates deeper understanding of additive mechanisms in PSCs.
  • Generative models show potential for novel additive identification.

Conclusions:

  • AI-driven approaches offer a more efficient and systematic method for additive discovery in PSCs.
  • The integration of AI with autonomous laboratories promises accelerated commercialization of PSC technology.