Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ $$\hbox {mol}^{-1}$$ , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ $$\hbox {mol}^{-1}$$ for MBE2 and 24.3 kJ $$\hbox {mol}^{-1}$$ for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ $$\hbox {mol}^{-1}$$ were observed at the MBE2 and MBE3 levels, respectively. Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

中文翻译：

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通过进化优化自动分子断裂


分子碎片是一套有效的方法，可以降低量子化学计算的形式计算复杂性，同时增强其算法的并行性。然而，由于缺乏自动化和评估分段方案质量的有效指标，分段技术的实际适用性仍然受到阻碍。在本文中，我们介绍了通过自动遗传搜索进行快速片段化（QFRAGS），这是一种新颖的自动片段化算法，该算法使用遗传优化程序来生成分子片段，在多体扩展（MBE）中采用时产生低能量错误。使用 HF/6-31G* 水平的二体 (MBE2) 和三体 (MBE3) MBE 计算对少于 500 个原子的蛋白质系统进行 QFRAGS 基准测试，结果显示平均绝对能量误差 (MAEE) 为 20.6分别为 2.2 kJ $$\hbox {mol}^{-1}$$ 。对于超过 500 个原子的较大蛋白质系统，MBE2 的 MAEE 为 181.5 kJ $$\hbox {mol}^{-1}$$，MBE3 的 MAEE 为 24.3 kJ $$\hbox {mol}^{-1}$$。此外，与 40 个蛋白质数据集上的三种手动片段化方案相比，使用 MBE 和片段分子轨道技术，QFRAGS 实现了相当或通常更低的 MAEE。当应用于 10-脂聚糖/糖脂数据集时，在 MBE2 和 MBE3 水平上分别观察到 7.9 和 0.3 kJ $$\hbox {mol}^{-1}$$ 的 MAE。科学贡献 本文介绍了通过自动遗传搜索进行快速片段化 (QFRAGS)，这是一种创新的分子片段化算法，通过专门解决现有分子片段化方法缺乏自动化和有效片段质量指标的问题，显着改进了现有分子片段化方法。 通过进化优化策略，QFRAGS 积极追求高质量碎片，生成在具有数百到数千个原子的系统上表现出最小能量误差的碎片方案。 QFRAGS 的出现代表了分子碎片技术的重大进步，极大地提高了精确量子化学计算的可访问性和计算可行性。