拓展原子分解与多体表示：一种受化学启发的、以单体为中心的机器学习势函数方法
Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
AI总结：
针对现有机器学习势函数化学可解释性不足、效率未达传统力场水平的问题，提出以单体为中心、结合PIP与前馈NN的新方法，在水与CO₂体系中实现精度与速度的前沿平衡，为复杂分子模拟提供新路径。
摘要：
目前凝聚相应用中最广泛使用的机器学习（ML）势函数多依赖于多体置换不变多项式（PIP）或以原子为中心的神经网络（NN）。但这些方法在原子能量分解方面通常缺乏化学可解释性，且尚未完全达到传统力场的计算效率。本文提出一种融合两类方法优势的新方法，实现了精度与力场级速度之间的前沿平衡。该方法采用以单体为中心的表示形式，将势能分解为具有化学意义的单体能量之和。无需复杂的神经网络设计，单体的结构描述符通过1体和2体有效相互作用来表征，并由适当的PIP集合作为前馈神经网络的输入加以约束。我们通过对气相水三聚体、液态水以及液态CO₂模型的系统评估验证了该方法的性能。该方法的高精度、快速度和灵活性为构建准确的机器学习势函数、实现复杂分子体系的大规模量子与经典模拟提供了新途径。
英文摘要：
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
