在经典力场中嵌入量子统计激发
Embedding quantum statistical excitations in a classical force field
摘要：
量子力学驱动的电荷极化和电荷转移在生物分子系统中普遍存在，控制反应速率、变构相互作用、配体-蛋白质结合、膜运输和动力学驱动的结构转变。这些过程的分子动力学（MD）模拟需要量子力学（QM）信息，以准确地描述其反应动力学。然而，目前的技术-经验力场，子系统方法，从头算MD和机器学习-在它们的能力不同，以实现跨多种原子类型的一致的化学描述，并在规模。在这里，我们提出了一个基于物理学的原子力场，系综DFT电荷转移嵌入原子方法，其中QM力在所有原子上以统一的理论水平描述，避免了对薛定谔方程或大型预先计算的训练数据集的显式解的需要。电子和原子的长度尺度之间的耦合是通过最初为元素材料开发的嵌入原子方法的系综密度泛函理论制定的影响。电荷转移用单个原子的离子态基密度的集合表示，而电荷极化用原子的激发态基密度表示。这提供了一个高度紧凑但一般的力场表示，包括局部和系统范围的影响。电荷重排通过系综权重的演变来实现，通过化学势均衡在每个动力学时间步进行调整。
英文摘要：
Quantum-mechanically-driven charge polarization and charge transfer are ubiquitous in biomolecular systems, controlling reaction rates, allosteric interactions, ligand-protein binding, membrane transport, and dynamically-driven structural transformations. Molecular dynamics (MD) simulations of these processes require quantum mechanical (QM) information in order to accurately describe their reactive dynamics. However, current techniques -- empirical force fields, subsystem approaches, ab initio MD, and machine learning -- vary in their ability to achieve a consistent chemical description across multiple atom types, and at scale. Here we present a physics-based, atomistic force field, the ensemble DFT charge-transfer embedded-atom method, in which QM forces are described at a uniform level of theory across all atoms, avoiding the need for explicit solution of the Schrödinger equation or large, precomputed training datasets. Coupling between the electronic and atomistic length scales is effected through an ensemble density functional theory formulation of the embedded atom method originally developed for elemental materials. Charge transfer is expressed in terms of ensembles of ionic states basis densities of individual atoms, and charge polarization, in terms of atomic excited state basis densities. This provides a highly compact yet general representation of the force field, encompassing both local and system-wide effects. Charge rearrangement is realized through the evolution of ensemble weights, adjusted at each dynamical timestep via chemical potential equalization.
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。
