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

• Published in: Wiley interdisciplinary reviews. Computational molecular science 2023-05, Vol.13 (3), p.e1650-n/a
• Main Authors: Liu, Jinfeng, He, Xiao
• Format: Article
• Language: English
• Subjects: ab initio molecular dynamics simulation; Anharmonicity; Biomolecules; Catalysis; Catalysts; Chemical reactions; Chromophores; Complex systems; Computer applications; Computing costs; Crystals; Electronic structure; excited state electronic structure; Fractionation; Fragmentation; fragment‐based QM method; Infrared radiation; infrared spectrum; Machine learning; Mathematical analysis; Methods; Molecular clusters; Molecular dynamics; neural network; Neural networks; Physicochemical processes; Physicochemical properties; Quantum mechanics; Reaction mechanisms; Scaling; Simulation; Wave functions
• ISSN: 1759-0876; 1759-0884
• DOI: 10.1002/wcms.1650

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. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Reaction Mechanisms and Catalysis Efficient and accurate quantum mechanical calculations on large complex systems are made possible via the fragmentation approach.