Model Reduction and Neural NetworksProtein Structure and DynamicsParallel Computing and Optimization Techniques

Xuewei Xiong, Xingyuan Lu, Yueyang Zhang, Zijing Wu, Fuming Ying, Wei Wu, Peifeng Su

2026.3.4CHINESE JOURNAL OF CHEMICAL PHYSICS

DOI: 10.1063/1674-0068/cjcp2510168

Abstract

Energy decomposition analysis (EDA) has become a powerful approach for elucidating chemical bonding and non-covalent interactions, especially in complex and multiscale systems. Embedding strategies combining high-level quantum chemical methods with more approximate treatments of the environment provide a promising balance between accuracy and efficiency, yet existing embedding schemes of EDA approaches implementations are often fragmented and software-dependent. To address this challenge, we present PyMEDA, a unified, open-source interface framework for multiscale energy decomposition analysis. Through a modular design, PyMEDA standardizes interfaces to diverse quantum mechanical, molecular mechanics, and semi-empirical backends (initially XEDA, OpenMM, and xtb), enabling the unified implementation of workflows for both conventional and embedding-type EDA schemes, such as DM-EDA (QM/MM) and DM-EDA (EB). By providing extensibility, reproducibility, and automation, PyMEDA offers a flexible environment to advance multiscale interaction analysis and facilitates the future development of embedding-based EDA methods.

Citation format

XIONG, Xuewei, et al. Pymeda: A unified python framework for multiscale energy decomposition analysis. CHINESE JOURNAL OF CHEMICAL PHYSICS, 2026, 39(2): 171–181.