标题： 通过多重极展开方法引入长程相互作用到基态和激发态分子模拟中 显示英文标题

标题： Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations

摘要： 模拟长程相互作用仍然是分子机器学习势能面临的一个重大挑战，因为需要准确捕捉大空间区域内的相互作用。 在本工作中，我们引入了FieldMACE，这是消息传递原子簇展开（MACE）架构的扩展，它集成了多极展开以更高效地建模长程相互作用。 通过引入多极展开， FieldMACE能够有效捕捉基态和激发态中的环境和长程效应。 基准评估表明，与之前架构相比，它在预测和计算效率方面表现出色，并且能够准确模拟非绝热激发态动力学。 此外，从基础模型进行迁移学习提高了数据效率，使FieldMACE成为一个可扩展、稳健且可迁移的大规模分子模拟框架。 显示英文摘要

摘要： Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial regions. In this work, we introduce FieldMACE, an extension of the message-passing atomic cluster expansion (MACE) architecture that integrates the multipole expansion to model long-range interactions more efficiently. By incorporating the multipole expansion, FieldMACE effectively captures environmental and long-range effects in both ground and excited states. Benchmark evaluations demonstrate its superior performance in predictions and computational efficiency compared to previous architectures, as well as its ability to accurately simulate nonadiabatic excited-state dynamics. Furthermore, transfer learning from foundational models enhances data efficiency, making FieldMACE a scalable, robust, and transferable framework for large-scale molecular simulations.