Biased molecular dynamics: Difference between revisions
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The Hamiltonian of the physical system: | |||
:<math> | |||
H(q,p) = T(p) + V(q), \; | |||
</math> | |||
with ''T''(''p''), and ''V''(''q'') being kinetic, and potential energies, respectively, can be extended by adding a bias potential <math>\tilde{V}(\xi)</math> acting only on one or more selected internal coordinates of the system ξ=ξ(''q''): | |||
:<math> | |||
\tilde{H}(q,p) = H(q,p) + \tilde{V}(\xi). | |||
</math> | |||
Presently, the following types of <math>\tilde{V}(\xi)</math> are supported: | |||
*Gauss function | |||
:<math> | |||
\tilde{V}(\xi)= h\,\text{exp}\left [-\frac{(\xi(q)-\xi_0)^2}{2w^2} \right ], \; | |||
</math> | |||
*harmonic potential | |||
:<math> | |||
\tilde{V}(\xi) = \frac{1}{2}\kappa (\xi(q)-\xi_0)^2 \; | |||
</math> | |||
*Fermi function | |||
:<math> | |||
\tilde{V}(\xi)= \frac{A}{1+\text{exp}\left [-D\frac{\xi(q)}{\xi_0} -1 \right ]} \; | |||
</math> | |||
The probability density for a geometric parameter ξ of the system driven by a Hamiltonian: | The probability density for a geometric parameter ξ of the system driven by a Hamiltonian: | ||
:<math> | :<math> |
Revision as of 09:30, 6 April 2023
The Hamiltonian of the physical system:
with T(p), and V(q) being kinetic, and potential energies, respectively, can be extended by adding a bias potential acting only on one or more selected internal coordinates of the system ξ=ξ(q):
Presently, the following types of are supported:
- Gauss function
- harmonic potential
- Fermi function
The probability density for a geometric parameter ξ of the system driven by a Hamiltonian:
with T(p), and V(q) being kinetic, and potential energies, respectively, can be written as:
The term stands for a thermal average of quantity X evaluated for the system driven by the Hamiltonian H.
If the system is modified by adding a bias potential acting only on a selected internal parameter of the system ξ=ξ(q), the Hamiltonian takes a form:
and the probability density of ξ in the biased ensemble is:
It can be shown that the biased and unbiased averages are related via a simple formula:
More generally, an observable :
can be expressed in terms of thermal averages within the biased ensemble:
Simulation methods such as umbrella sampling[1] use a bias potential to enhance sampling of ξ in regions with low P(ξi) such as transition regions of chemical reactions. The correct distributions are recovered afterwards using the equation for above.
A more detailed description of the method can be found in Ref.[2]. Biased molecular dynamics can be used also to introduce soft geometric constraints in which the controlled geometric parameter is not strictly constant, instead it oscillates in a narrow interval of values.
Supported types of bias potentials
Presently, the following types of bias potential are supported:
- Gauss function
- harmonic potential
- Fermi function
Andersen thermostat
- For a biased molecular dynamics run with Andersen thermostat, one has to:
- Set the standard MD-related tags: IBRION=0, TEBEG, POTIM, and NSW
- Set MDALGO=1 (MDALGO=11 in VASP 5.x), and choose an appropriate setting for ANDERSEN_PROB
- In order to avoid updating of the bias potential, set HILLS_BIN=NSW
- Define collective variables in the ICONST-file, and set the STATUS parameter for the collective variables to 5
- Define the bias potential in the PENALTYPOT-file
Nose-Hoover thermostat
- For a biased molecular dynamics run with Nose-Hoover thermostat, one has to:
- Set the standard MD-related tags: IBRION=0, TEBEG, POTIM, and NSW
- Set MDALGO=2 (MDALGO=21 in VASP 5.x), and choose an appropriate setting for SMASS
- In order to avoid updating of the bias potential, set HILLS_BIN=NSW
- Define collective variables in the ICONST-file, and set the STATUS parameter for the collective variables to 5
- Define the bias potential in the PENALTYPOT-file
The values of all collective variables for each MD step are listed in the REPORT-file, check the lines after the string Metadynamics.
References
- ↑ G. M. Torrie and J. P. Valleau, J. Comp. Phys. 23, 187 (1977).
- ↑ D. Frenkel and B. Smit, Understanding molecular simulations: from algorithms to applications, Academic Press: San Diego, 2002.