MAPLE: A General Framework for Automated Molecular Modeling Across Machine Learning Potentials
This paper presents MAPLE as a comprehensive framework for automated molecular modeling that works seamlessly across different machine learning potentials. The framework enables researchers to perform molecular simulations and quantum chemical calculations using various ML-based potential energy surfaces, providing a unified interface for diverse computational chemistry applications.