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首页> 外文期刊>Physica Scripta: An International Journal for Experimental and Theoretical Physics >Molecular simulation by knowledgeable quantum atoms
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Molecular simulation by knowledgeable quantum atoms

机译：通过知识渊博的量子原子进行分子模拟

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We are at the dawn of molecular simulations being carried out, literally, by atoms endowed by knowledge of how to behave quantum mechanically in the vicinity of other atoms. The 'next-next-generation' force field that aims to achieve this is called QCTFF, for now, although a more pronounceable name will be suggested in the conclusion. Classical force fields such as AMBER mimic the interatomic energy experienced by atoms during a molecular simulation, with simple expressions capturing a relationship between energy and nuclear position. Such force fields neither see the electron density nor exchange-delocalization itself, or exact electrostatic interaction; they only contain simple equations and elementary parameters such as point charges to imitate the energies between atoms. Next-generation force fields, such as AMOEBA, go further and make the electrostatics more accurate by introducing multipole moments and dipolar polarization. However, QCTFF goes even further and abolishes all traditional force field expressions (e.g. Hooke's law and extensions, Lennard-Jones) in favor of atomistic kriging models. These machine learning models learn how fundamental energy quantities, as well as high-rank multipole moments, all associated with an atom of interest, vary with the precise positions of atomic neighbors. As a result, all structural phenomena can be rapidly calculated as an interplay of intra-atomic energy, exchange-delocalization energy, electrostatic energy and dynamic correlation energy. The final QCTFF force field will generate a wealth of localized quantum information while being faster than a Car-Parrinello simulation (which does not generate local information).
机译：从字面上看，我们是由具有如何在其他原子附近机械地量子化的知识所赋予的原子进行分子模拟的曙光。目前，旨在实现这一目标的“下一代”力场被称为QCTFF，尽管结论中会提出一个更明显的名称。经典的力场（例如AMBER）模仿分子在分子模拟过程中所经历的原子间能量，并通过简单的表达式捕获能量与核位置之间的关系。这样的力场既看不到电子密度，也看不到交换离域，也看不到确切的静电相互作用。它们只包含简单的方程式和基本参数，例如点电荷，以模仿原子之间的能量。下一代力场，例如AMOEBA，可以通过引入多极矩和偶极极化来进一步提高静电强度。但是，QCTFF甚至走得更远，废除了所有传统的力场表达式（例如胡克定律和扩展名Lennard-Jones），转而采用原子克里金模型。这些机器学习模型学习与关注原子相关联的基本能量以及高阶多极矩如何随原子邻居的精确位置而变化。结果，可以将所有结构现象迅速地计算为原子内能量，交换离域能，静电能和动态相关能之间的相互作用。最终的QCTFF力场将生成大量的局部量子信息，同时比Car-Parrinello模拟（不生成局部信息）更快。

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