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Article Dans Une Revue Journal of Chemical Information and Modeling Année : 2018

Semi-Empirical Born–Oppenheimer Molecular Dynamics (SEBOMD) within the Amber Biomolecular Package

Résumé

Semi-empirical quantum methods from the neglect of differential diatomic overlap (NDDO) family such as MNDO, AM1, or PM3 are fast albeit approximate quantum methods. By combining them with linear scaling methods like the divide & conquer (D&C) method, it is possible to quickly evaluate the energy of systems containing hundreds to thousands of atoms. We here present our implementation in the Amber biomolecular package of a SEBOMD module that provides a way to run semi-empirical Born−Oppenheimer molecular dynamics. At each step of a SEBOMD, a fully converged self-consistent field (SCF) calculation is performed to obtain the semiempirical quantum potential energy of a molecular system encaged or not in periodic boundary conditions. We describe the implementation and the features of our SEBOMD implementation. We show the requirements to conserve the total energy in NVE simulations, and how to accelerate SCF convergence through density matrix extrapolation. Specific ways of handling periodic boundary conditions using mechanical embedding or electrostatic embedding through a tailored quantum Ewald summation is developed. The parallel performance of SEBOMD simulations using the D&C scheme are presented for liquid water systems of various sizes, and a comparison between the traditional full diagonalization scheme and the D&C approach for the reproduction of the structure of liquid water illustrates the potentiality of SEBOMD to simulate molecular systems containing several hundreds of atoms for hundreds of picoseconds with a quantum mechanical potential in a reasonable amount of CPU time.
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Dates et versions

hal-02188215 , version 1 (18-07-2019)

Identifiants

Citer

Antoine Marion, Hatice Gökcan, Gerald Monard. Semi-Empirical Born–Oppenheimer Molecular Dynamics (SEBOMD) within the Amber Biomolecular Package. Journal of Chemical Information and Modeling, 2018, 59 (1), pp.206-214. ⟨10.1021/acs.jcim.8b00605⟩. ⟨hal-02188215⟩
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