Using Machine Learning to Greatly Accelerate Path Integral Molecular Dynamics

Overview

Journal J Chem Theory Comput

Publisher American Chemical Society

Specialties Biochemistry
Chemistry

Date 2022 Jan 4

PMID 34982562

Citations 6

Authors

Chenghan Li

Gregory A Voth

Affiliations

Soon will be listed here.

Abstract

molecular dynamics (AIMD) has become one of the most popular and robust approaches for modeling complicated chemical, liquid, and material systems. However, the formidable computational cost often limits its widespread application in simulations of the largest-scale systems. The situation becomes even more severe in cases where the hydrogen nuclei may be better described as quantized particles using a path integral representation. Here, we present a computational approach that combines machine learning with recent advances in path integral contraction schemes, and we achieve a 2 orders of magnitude acceleration over direct path integral AIMD simulation while at the same time maintaining its accuracy.

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References

Napoli J, Marsalek O, Markland T . Decoding the spectroscopic features and time scales of aqueous proton defects. J Chem Phys. 2018; 148(22):222833. DOI: 10.1063/1.5023704. View

Tuckerman , Marx , Klein , Parrinello . On the Quantum Nature of the Shared Proton in Hydrogen Bonds. Science. 1997; 275(5301):817-20. DOI: 10.1126/science.275.5301.817. View

Gaus M, Cui Q, Elstner M . DFTB3: Extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB). J Chem Theory Comput. 2012; 7(4):931-948. PMC: 3509502. DOI: 10.1021/ct100684s. View

Lee , Yang , PARR . Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B Condens Matter. 1988; 37(2):785-789. DOI: 10.1103/physrevb.37.785. View

Steele R . Communication: Multiple-timestep ab initio molecular dynamics with electron correlation. J Chem Phys. 2013; 139(1):011102. DOI: 10.1063/1.4812568. View

Goyal P, Elstner M, Cui Q . Application of the SCC-DFTB method to neutral and protonated water clusters and bulk water. J Phys Chem B. 2011; 115(20):6790-805. PMC: 3101025. DOI: 10.1021/jp202259c. View

Calio P, Hocky G, Voth G . Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Molecular Dynamics Simulations of Water. J Chem Theory Comput. 2020; 16(9):5675-5684. DOI: 10.1021/acs.jctc.0c00558. View

Cheng X, Herr J, Steele R . Accelerating Ab Initio Path Integral Simulations via Imaginary Multiple-Timestepping. J Chem Theory Comput. 2016; 12(4):1627-38. DOI: 10.1021/acs.jctc.6b00021. View

Buxton S, Habershon S . Accelerated path-integral simulations using ring-polymer interpolation. J Chem Phys. 2017; 147(22):224107. DOI: 10.1063/1.5006465. View

10.

Hammes-Schiffer S . Quantum-classical simulation methods for hydrogen transfer in enzymes: a case study of dihydrofolate reductase. Curr Opin Struct Biol. 2004; 14(2):192-201. DOI: 10.1016/j.sbi.2004.03.008. View

11.

Cassone G . Nuclear Quantum Effects Largely Influence Molecular Dissociation and Proton Transfer in Liquid Water under an Electric Field. J Phys Chem Lett. 2020; 11(21):8983-8988. DOI: 10.1021/acs.jpclett.0c02581. View

12.

Zhang L, Han J, Wang H, Car R, E W . Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics. Phys Rev Lett. 2018; 120(14):143001. DOI: 10.1103/PhysRevLett.120.143001. View

13.

Kapil V, VandeVondele J, Ceriotti M . Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods. J Chem Phys. 2016; 144(5):054111. DOI: 10.1063/1.4941091. View

14.

Grimme S, Antony J, Ehrlich S, Krieg H . A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys. 2010; 132(15):154104. DOI: 10.1063/1.3382344. View

15.

Marsalek O, Markland T . Ab initio molecular dynamics with nuclear quantum effects at classical cost: Ring polymer contraction for density functional theory. J Chem Phys. 2016; 144(5):054112. DOI: 10.1063/1.4941093. View

16.

Marsalek O, Markland T . Quantum Dynamics and Spectroscopy of Ab Initio Liquid Water: The Interplay of Nuclear and Electronic Quantum Effects. J Phys Chem Lett. 2017; 8(7):1545-1551. DOI: 10.1021/acs.jpclett.7b00391. View

17.

Rossi M, Ceriotti M, Manolopoulos D . How to remove the spurious resonances from ring polymer molecular dynamics. J Chem Phys. 2014; 140(23):234116. DOI: 10.1063/1.4883861. View

18.

Ceriotti M, Fang W, Kusalik P, McKenzie R, Michaelides A, Morales M . Nuclear Quantum Effects in Water and Aqueous Systems: Experiment, Theory, and Current Challenges. Chem Rev. 2016; 116(13):7529-50. DOI: 10.1021/acs.chemrev.5b00674. View

19.

Becke . Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A Gen Phys. 1988; 38(6):3098-3100. DOI: 10.1103/physreva.38.3098. View

20.

Markland T, Manolopoulos D . An efficient ring polymer contraction scheme for imaginary time path integral simulations. J Chem Phys. 2008; 129(2):024105. DOI: 10.1063/1.2953308. View