Machine learning interatomic potentials at the centennial crossroads of quantum mechanics

Area of Science:

• Computational chemistry
• Materials science
• Quantum mechanics

Background:

• Machine learning interatomic potentials (MLIPs) are emerging tools in molecular modeling.
• They bridge the gap between quantum mechanical accuracy and classical computational efficiency.
• The centennial of quantum mechanics in 2025 highlights the importance of these advancements.

Purpose of the Study:

• To examine the development of MLIPs.
• To address key challenges in their application.
• To outline future directions for next-generation computational chemistry.

Main Methods:

• Reviewing architectural innovations in MLIPs.
• Exploring physics-informed machine learning approaches.
• Analyzing foundation models trained on extensive datasets.

Main Results:

• Significant progress has been made in achieving chemical accuracy with MLIPs.
• Computational efficiency has been maintained and improved.
• Interpretability and generalizability are active areas of research and development.

Conclusions:

• MLIPs are rapidly advancing computational chemistry.
• Innovations focus on accuracy, efficiency, interpretability, and generalizability.
• Future MLIP frameworks will be predictive, transferable, and physically grounded.