A density fitting scheme for the fast evaluation of molecular electrostatic potential. Issue 7 (21st November 2022)

• Record Type: Journal Article
• Authors: Zhang, Yingfeng; Zhao, Jian
• Is Part Of: Journal of computational chemistry. Volume 44:Issue 7(2023)
• Page Start: 806
• Page End: 813
• Publication Date: 2022-11-21
• Subjects: density fitting -- molecular electrostatic potential; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.27042
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract: Molecular electrostatic potential (MEP) is a significant and crucial physical quantity that can be applied to a large number of scenarios, such as the prediction of nucleophilic or electrophilic attacks, fitting atomic charges, σ‐hole, and so forth. The computational cost for the MEP has an O ( N 2 ) scaling with the increase of atoms, which is intractable and laborious for macromolecules. Herein, a density fitting molecular electrostatic potential (DF‐MEP) is used to reduce the computational costs for the macromolecular MEP. It is found that the accuracy of DF‐MEP is almost identical to the conventional molecular electrostatic potential (Conv‐MEP), while the computational costs can be reduced to an O ( N ) scaling, for example, the computational time of 699, 200 grids for the Trp‐cage molecule (304 atoms) only takes 16.6 s at the B3LYP‐D3(BJ)/def2‐SVP level of theory with 16 CPU cores compared with 3060.2 s for the Conv‐MEP method. Abstract : The density fitting (DF) method reduces the scale of molecular electrostatic potential (MEP) from O ( N 2 ) to O ( N ), which can gain the same MEP map as the conventional MEP (Conv‐MEP) method with much less computational cost in 16‐cores CPU.