Using collective variables to drive molecular dynamics simulations

A software framework is introduced that facilitates the application of biasing algorithms to collective variables of the type commonly employed to drive massively parallel molecular dynamics (MD) simulations. The modular framework that is presented enables one to combine existing collective variables into new ones, and combine any chosen collective variable with available biasing methods. The latter include the classic time-dependent biases referred to as steered MD and targeted MD, the temperature-accelerated MD algorithm, as well as the adaptive free-energy biases called metadynamics and adaptive biasing force. The present modular software is extensible, and portable between commonly used MD simulation…

• NS
  National Science Foundation

  Awards: 1212416, CHE-1212416
• GÉ
  Grand Équipement National De Calcul Intensif
• DO
  Division of Chemistry

  Award: CHE-1212416