基于模板的自动碎片化算法,用于基于能源的一般化碎片化框架中的复杂和大型系统
Xuerong Wang1, Junhui Sun1, Linke He1
1State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Jiangsu Key Laboratory of Clean Energy Catalysis and Intelligent Green Chemical Engineering, New Cornerstone Science Laboratory, School of Chemistry, Nanjing University, Nanjing 210023, People's Republic of China.
The Journal of chemical physics
|February 9, 2026
概括
我们为大分子开发了一种自动化碎片化算法,大大降低了量子化学计算中的计算成本和手工精力. 这种方法实现了高精度,使得复杂的分子模拟更快,更容易获得.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 分子建模分子建模
背景情况:
- 基于能源的碎片化方法通常需要人工干预,导致效率低下和不一致.
- 将量子化学计算扩展到大型和复杂的分子系统仍然是一个重大挑战.
研究的目的:
- 开发基于模板的自动碎片化算法,以克服现有方法的局限性.
- 扩展基于能源的一般化碎片化 (GEBF) 方法,以有效地分割各种分子系统.
主要方法:
- 实现了一个层次化的SMILES编码的GEBF模板库,用于循环和非循环函数组.
- 使用结构转换,宏循环检测,亚结构匹配和小碎片合并进行分区.
- 控制碎片大小以平衡精度和计算成本,并提供用户定义模板的选项.
主要成果:
- 对各种复杂分子 (生物宏分子,宏循环等) 实现了与传统方法相比的量子化学结果. ) 的情况.
- 将最大的子系统基础大小减少了三分之二以上,使大系统 (例如1500个原子的寡合体) 的实际计算成为可能.
- 验证了GEBF力,用于准确的几何优化,光谱预测和反应能量计算.
结论:
- 开发的算法可实现大规模应用的全自动化,可扩展和准确的量子化学.
- 这一进步通过降低计算成本,弥合了理论化学和现实世界应用之间的差距.
- 该方法为复杂系统提供了高准确度的预测,包括酶中的反应机制.
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