Machine Learning Force Fields: Recent Advances and Remaining Challenges.

Area of Science:

• Chemistry and Physics
• Computational Science
• Materials Science

Background:

• Machine learning (ML) methods offer significant potential for advancing scientific understanding and modeling capabilities in chemistry and physics.
• The increasing availability of user-friendly ML packages facilitates the application of these techniques to atomistic systems.
• Understanding complex molecules and materials requires sophisticated modeling approaches.

Purpose of the Study:

• To provide a comprehensive overview of ML techniques for developing ML force fields.
• To discuss commonalities between ML modeling and quantum-mechanical approximations.
• To highlight recent advancements and future directions in ML-driven molecular modeling.

Main Methods:

• Discussion of general aspects of ML techniques in the context of ML force field development.
• Comparison of global and local ML models, including their underlying physical principles.
• Review of recent developments and emerging trends in ML-driven molecular modeling.

Main Results:

• ML methods are increasingly valuable tools for modeling complex molecules and materials.
• ML force fields can be developed by leveraging common features with quantum-mechanical approximations.
• Global and local ML models represent distinct approaches with different physical underpinnings.

Conclusions:

• ML-driven molecular modeling is a rapidly evolving field with transformative potential.
• Interdisciplinary collaboration between physical chemistry, chemical physics, computer science, and data science is crucial for future progress.
• This perspective aims to foster such collaborations by outlining current ML methodologies and future opportunities.