Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces

Key Points

• This research aims to address the challenges of generalization in machine learning interatomic potentials for diverse chemical environments.
• Development of machine learning models for interatomic potentials.
• Simulation of various chemical environments to test generalization capabilities.
• Analysis of the accuracy of MLIPs compared to quantum mechanical calculations.
• Machine learning potentials demonstrate improved accuracy over traditional methods in diverse chemical spaces.
• Generalization capabilities vary significantly across different chemical environments.

Abstract