单分子机器学习势能用于一般共价分子：线性烷烃为例
Monomeric machine learning potential for general covalent molecules: linear alkanes as an example
AI总结：
本文提出MB-PIPNet框架，通过单分子能量分解与PIP描述符结合神经网络，实现高效且高精度的分子模拟，以线性烷烃为例验证其性能。
摘要：
机器学习势能（MLPs）已成为现代分子模拟的重要工具。然而，开发同时实现高精度和高计算效率的模型仍然是一个重大挑战。在本工作中，我们扩展了最近提出的MB-PIPNet框架，通过结合基于单分子的能量分解、置换不变多项式（PIP）描述符和神经网络，将其应用于一般共价键合分子系统。在该框架中，总势能被表示为有效单分子贡献的总和，其中PIP提供紧凑且化学驱动的单分子内部结构及其局部化学环境的描述。作为概念验证应用，我们将MB-PIPNet框架应用于线性烷烃，使用十四烷作为代表性系统，并将其性能与已建立的原子机器学习模型进行基准测试。所得MB-PIPNet势能能够准确再现参考的从头算电子能，并可靠地捕捉到关键分子性质，包括扭摆势能曲线、谐波振动频率以及从分子动力学模拟中获得的振动功率谱。重要的是，MB-PIPNet在结合能量和力评估的计算效率上显著优于其他MLP模型。这些结果确立了MB-PIPNet作为构建MLP的可扩展且高效的框架，为复杂分子系统的大规模量子和经典模拟提供了另一条途径。
英文摘要：
Machine-learning potentials (MLPs) have become important tools for modern molecular simulations. However, developing models that simultaneously achieve high accuracy and high computational efficiency remains a significant challenge. In this work, we extend the recently proposed MB-PIPNet framework to general covalently bonded molecular systems by combining monomer-based energy decomposition, permutationally invariant polynomial (PIP) descriptors, and neural networks within a fragmentation-based strategy. Within this framework, the total potential energy is represented as a sum of effective monomeric contributions, where PIPs provide compact and chemically motivated descriptions of both monomer internal structures and their local chemical environments. As a proof-of-concept application, we apply the MB-PIPNet framework to linear alkanes, using n-Tetradecane as a representative system, and benchmark its performance against established atomistic machine-learning models. The resulting MB-PIPNet potential accurately reproduces reference ab initio electronic energies and reliably captures key molecular properties, including torsional potential energy profiles, harmonic vibrational frequencies, and vibrational power spectra obtained from molecular dynamics simulations. Importantly, MB-PIPNet demonstrates a substantial advantage in computational efficiency over other MLP models for combined energy and force evaluations. These results establish MB-PIPNet as a scalable and efficient framework for constructing MLPs, providing an additional route for large-scale quantum and classical simulations of complex molecular systems.
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