Recent advances in quantum fragmentation approaches to complex molecular and condensed-phase systems

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jinfeng Liu, Xiao He
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引用次数: 5

Abstract

Quantum mechanical (QM) calculations are critical in quantitatively understanding the relationship between the structure and physicochemical properties of various chemical systems. However, the sharply increasing computational cost with the system size has severely hindered applying direct QM calculations on large-sized systems. Hence, linear-scaling and/or fragmentation QM methods have been proposed to overcome this difficulty. In this review, we focus on the recent development and applications of the electrostatically embedded generalized molecular fractionation with the conjugate caps (EE-GMFCC) method in probing various properties of complex large molecules and condensed-phase systems. The EE-GMFCC method is now capable of describing the localized excited states of biomolecules and molecular crystals with a chromophore. The EE-GMF method is also combined with anharmonic vibrational calculations for accurate simulation of the infrared spectrum of the magic number H+(H2O)21 cluster at the coupled cluster level. With an adaptive fragmentation scheme, the EE-GMF-based ab initio molecular dynamics is able to directly simulate chemical reactions occurred in atmospheric molecular clusters. Furthermore, by combining the EE-GMF(CC) method and deep machine learning techniques, neural network potentials can be efficiently constructed for accurate simulations of complex systems with the accuracy of high-level wave function methods. The EE-GMF(CC) method is expected to become a practical tool for quantitative description of complex large molecules and condensed-phase systems with high-level ab initio theories or ab initio quality potentials.

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Abstract Image

复杂分子和凝聚相体系的量子碎片化研究进展
量子力学(QM)计算对于定量理解各种化学体系的结构和物理化学性质之间的关系至关重要。然而,随着系统规模的增大,计算成本急剧增加,这严重阻碍了在大型系统上直接进行QM计算。因此,提出了线性缩放和/或碎片化QM方法来克服这一困难。本文综述了近年来静电嵌入共轭帽广义分子分馏(EE-GMFCC)方法在探测复杂大分子和凝聚相体系各种性质方面的研究进展和应用。EE-GMFCC方法现在能够用发色团描述生物分子和分子晶体的局部激发态。结合EE-GMF方法,在耦合团簇水平上精确模拟了幻数H+(H2O)21团簇的红外光谱。基于e - gmf的从头算分子动力学采用自适应碎片化方案,能够直接模拟大气分子簇中发生的化学反应。此外,通过将EE-GMF(CC)方法与深度机器学习技术相结合,可以有效地构建神经网络电位,以具有高级波函数方法的精度来精确模拟复杂系统。EE-GMF(CC)方法有望成为具有高水平从头算理论或从头算质量势的复杂大分子和凝聚相系统定量描述的实用工具。本文分类如下:
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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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