基于伸长率法的交替多向自动性能优化过程及其应用

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Shichen Lin, Yuuichi Orimoto, Yuriko Aoki
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引用次数: 0

摘要

基于伸长率(ELG)方法的性能优化(POPT)是设计从一端到另一端的大型系统的有效方法。介绍了一种交替多向ELG方法,实现了多生长方向复杂体系的交替POPT,提高了效率。通过交替POPT优化沿z $$ z $$ -轴排列的给体-受体取代聚二乙炔(PDA)的(超)极化能力,其中供体-受体取代的二乙炔单体交替选择并连接到两个PDA终端。交替POPT在设计具有预期性能、效率和精度的系统方面的能力已得到验证。对于n种$$ n $$类型的供体和受体组,现有的同时基于eeg的POPT需要计算n2 $$ {n}^2 $$组合,由于单体在两个延伸方向上同时附着。相比之下,基于elg的交替POPT只需要2 n次$$ 2n $$组合,计算中涉及的基函数数量减少了一半,显著提高了效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Elongation Method-Based Alternating Multi-Directional Automated Property Optimization Process and Its Application

Elongation Method-Based Alternating Multi-Directional Automated Property Optimization Process and Its Application

Elongation (ELG) method-based property optimization (POPT) is an effective approach for designing large systems from one terminal to the other. An alternating multi-directional ELG method is introduced to enable alternating POPT for complex systems with multiple growth directions, improving efficiency. (Hyper)polarizabilities of donor-acceptor-substituted polydiacetylenes (PDAs) aligned along the z $$ z $$ -axis are optimized by alternating POPT, where donor- and acceptor-substituted diacetylene monomers are alternately selected and attached to two PDA terminals. Alternating POPT's capability in designing systems with expected properties, efficiency, and accuracy has been validated. For n $$ n $$ types of donor and acceptor groups, the existing simultaneous ELG-based POPT requires calculating n 2 $$ {n}^2 $$ combinations, as monomers are simultaneously attached in both elongation directions. In contrast, the alternating ELG-based POPT only requires 2 n $$ 2n $$ combinations, halving the number of basis functions involved in calculations and significantly enhancing efficiency.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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