Your browser doesn't support javascript.
loading
An Efficient Random Number Generation Method for Molecular Simulation.
Okada, Kiyoshiro; Brumby, Paul E; Yasuoka, Kenji.
Afiliação
  • Okada K; Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan.
  • Brumby PE; Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan.
  • Yasuoka K; Department of Mechanical Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan.
J Chem Inf Model ; 62(1): 71-78, 2022 01 10.
Article em En | MEDLINE | ID: mdl-34951306
ABSTRACT
We propose a new random number generation method, which is the fastest and the simplest of its kind, for use with molecular simulation. We also discuss the possibility of using this method with various other numerical calculations. To demonstrate the significant increases in calculation speeds that can be gained by using our method, we present a comparison with prior methods for dissipative particle dynamics (DPD) simulations. The DPD method uses random numbers to reproduce thermal fluctuations of molecules. As such, an efficient method to generate random numbers in parallel computing environments has been widely sought after. Several random number generation methods have been developed that use encryption. In this study, we establish for the first time that random numbers with desirable properties exist in the particle coordinates used in DPD calculations. We propose a method for generating random numbers without encryption that utilizes this source of randomness. This is an innovative method with minimal computational cost, since it is not dependent on a complicated random number generation algorithm or an encryption process. Furthermore, our method may lead to faster random number generation for many other physical and chemical simulations.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Clinical_trials Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Clinical_trials Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão
...