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Published June 20, 1996 | public
Journal Article

Constant Temperature Constrained Molecular Dynamics:  The Newton−Euler Inverse Mass Operator Method

Abstract

The Newton-Euler inverse mass operator (NEIMO) method for internal coordinate molecular dynamics (MD) of macromolecules (proteins and polymers) leads to stable dynamics for time steps about 10 times larger than conventional dynamics (e.g., 20 or 30 fs rather than 1 or 2 fs for systems containing hydrogens). NEIMO is practical for large systems since the computation time scales linearly with the number of degrees of freedom N (instead of the N^3 scaling for conventional constrained MD methods). In this paper we generalize the NEIMO formalism to the Nosé (and Hoover) thermostat to derive the Nosé and Hoover equations of motion for constrained canonical ensemble molecular dynamics. We also examined the optimum mass, Q, determining the time scale (τ_s) for exchange of energy with the heat bath for NEIMO-Hoover dynamics of polymers. We carried out NEIMO-Hoover simulations on the amorphous polymers poly(vinyl chloride) and poly(vinylidene fluoride), where we find that time steps of 20-30 fs lead to stable dynamics (10 times larger than for Cartesian dynamics). The computational efficiency of the NEIMO canonical MD method should make it a powerful tool for MD simulations of macromolecular materials.

Additional Information

© 1996 American Chemical Society. Received: October 13, 1995; In Final Form: April 13, 1996. The research was funded by NSF (CHE 94-13930 and ACS 92-17368). The facilities of the MSC are also supported by grants from DOE-BCTR, Asahi Chemical, Asahi Glass, BP Chemical, Chevron Petroleum Technology, BF Goodrich, Xerox, Hughes Research Lab., and Beckman Institute. This work has been partially performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Some of the computations were carried out at the JPL CRAY facility, NSF Pittsburgh Supercomputing Center, and NSF San Diego Supercomputing Center.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023