Machine learning meets chemical physics

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.