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Machine learning meets chemical physics.

Michele Ceriotti1, Cecilia Clementi2, O Anatole von Lilienfeld3

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Statistical learning, including machine learning, is increasingly used in chemistry. This editorial explores its growing role in physical chemistry, bridging empiricism and theory.

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Area of Science:

  • Physical chemistry
  • Chemical physics
  • Statistical learning

Background:

  • The application of statistical learning techniques to chemical problems has significantly increased.
  • Physical chemistry traditionally favored physics-based theory over empiricism.

Discussion:

  • This special issue guest editorial provides a rationale for the growing use of machine learning in chemical physics.
  • It offers an overview of the topics covered within the issue.
  • The editorial discusses the intersection of machine learning and chemical physics.

Key Insights:

  • Machine learning is becoming a powerful tool in physical chemistry.
  • There is a shift towards integrating data-driven approaches with traditional theoretical methods.
  • The synergy between machine learning and chemical physics is highlighted.

Outlook:

  • The continued integration of statistical learning is expected to advance chemical problem-solving.
  • Future research will likely focus on novel applications and theoretical underpinnings.
  • This trend promises to reshape the landscape of chemical research and discovery.