Biased molecular dynamics: Difference between revisions

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The Hamiltonian of the physical system:
''Biased molecular dynamics''' (MD) refers to advanced [[:Category:Molecular dynamics|MD-simulation methods]] that introduce a ''bias potential''. One of the most important purposes of using bias potentials is to enhance the sampling of phase space with low probability density (e.g., transition regions of chemical reactions). Depending on the type of sampling and in combination with the corresponding statistical methods one then has access to important thermodynamic quantities like, e.g., free energies. Biased molecular dynamics comes in very different flavors such as, e.g., umbrella sampling{{cite|torrie:jcp:1977}} and umbrella integration{{cite|kaestner:jcp:2005}}, to name a few. For a comprehensive description (especially about umbrella sampling), we refer the interested user to Ref. {{cite|frenkel:ap-book:2002}} written by D. Frenkel and B. Smit.
:<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 &xi;=&xi;(''q''):
:<math>
\tilde{H}(q,p) = H(q,p) + \tilde{V}(\xi).
</math>
 


 
The probability density for a geometric parameter &xi; of the system driven by a Hamiltonian
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 &xi; of the system driven by a Hamiltonian:
:<math>
:<math>
H(q,p) = T(p) + V(q), \;
H(q,p) = T(p) + V(q), \;
</math>
</math>
with ''T''(''p''), and ''V''(''q'') being kinetic, and potential energies, respectively, can be written as:
with ''T''(''p''), and ''V''(''q'') being kinetic, and potential energies, respectively, can be written as
:<math>
:<math>
P(\xi_i)=\frac{\int\delta\Big(\xi(q)-\xi_i\Big) \exp\left\{-H(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-H(q,p)/k_B\,T\right\}dq\,dp} =
P(\xi_i)=\frac{\int\delta\Big(\xi(q)-\xi_i\Big) \exp\left\{-H(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-H(q,p)/k_B\,T\right\}dq\,dp} =
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The term <math>\langle X \rangle_H</math> stands for a thermal average of quantity ''X'' evaluated for the system driven by the Hamiltonian ''H''.
The term <math>\langle X \rangle_H</math> 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 <math>\tilde{V}(\xi)</math> acting only on a selected internal parameter of the system &xi;=&xi;(''q''), the Hamiltonian takes a form:
If the system is modified by adding a bias potential <math>\tilde{V}(\xi)</math> acting on one or multiple selected internal coordinates of the system &xi;=&xi;(''q''), the Hamiltonian takes the form
:<math>
:<math>
\tilde{H}(q,p) = H(q,p) + \tilde{V}(\xi),
\tilde{H}(q,p) = H(q,p) + \tilde{V}(\xi),
</math>
</math>
and the probability density of &xi; in the biased ensemble is:
and the probability density of &xi; in the biased ensemble is
:<math>
:<math>
\tilde{P}(\xi_i)= \frac{\int  \delta\Big(\xi(q)-\xi_i\Big) \exp\left\{-\tilde{H}(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-\tilde{H}(q,p)/k_B\,T\right\}dq\,dp} =  \langle\delta\Big(\xi(q)-\xi_i\Big)\rangle_{\tilde{H}}
\tilde{P}(\xi_i)= \frac{\int  \delta\Big(\xi(q)-\xi_i\Big) \exp\left\{-\tilde{H}(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-\tilde{H}(q,p)/k_B\,T\right\}dq\,dp} =  \langle\delta\Big(\xi(q)-\xi_i\Big)\rangle_{\tilde{H}}.
</math>
</math>
It can be shown that the biased and unbiased averages are related via a simple formula:
It can be shown that the biased and unbiased averages are related via
:<math>
:<math>
P(\xi_i)=\tilde{P}(\xi_i) \frac{\exp\left\{\tilde{V}(\xi)/k_B\,T\right\}}{\langle \exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}.
P(\xi_i)=\tilde{P}(\xi_i) \frac{\exp\left\{\tilde{V}(\xi)/k_B\,T\right\}}{\langle \exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}.
</math>
</math>
More generally, an observable <math>\langle A \rangle_{H}</math>:
More generally, an observable
:<math>
:<math>
\langle A \rangle_{H} = \frac{\int  A(q) \exp\left\{-H(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-H(q,p)/k_B\,T\right\}dq\,dp}
\langle A \rangle_{H} = \frac{\int  A(q) \exp\left\{-H(q,p)/k_B\,T\right\} dq\,dp}{\int  \exp\left\{-H(q,p)/k_B\,T\right\}dq\,dp}
</math>
</math>
can be expressed in terms of thermal averages within the biased ensemble:
can be expressed in terms of thermal averages within the biased ensemble as
:<math>
:<math>
\langle A \rangle_{H} =\frac{\langle A(q) \,\exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}{\langle \exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}.
\langle A \rangle_{H} =\frac{\langle A(q) \,\exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}{\langle \exp\left\{\tilde{V}(\xi)/k_B\,T\right\} \rangle_{\tilde{H}}}.
</math>
</math>
Simulation methods such as umbrella sampling<ref name="Torrie77"/> use a bias potential to enhance sampling of &xi; in regions with low ''P''(&xi;<sub>''i''</sub>) such as transition regions of chemical
reactions.
The correct distributions are recovered afterwards using the equation for <math>\langle A \rangle_{H}</math> above.
A more detailed description of the method can be found in Ref.<ref name="FrenkelSmit"/>.
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
:<math>
V_{bias}(\xi) = h\,\text{exp}\left [-\frac{(\xi(q)-\xi_0)^2}{2w^2}  \right ], \;
</math>
*harmonic potential
:<math>
V_{bias}(\xi) = \frac{1}{2}\kappa (\xi(q)-\xi_0)^2 \;
</math>
*Fermi function
:<math>
V_{bias}(\xi) = \frac{A}{1+\text{exp}\left [-D\frac{\xi(q)}{\xi_0} -1 \right ]} \;
</math>
== Andersen thermostat ==
* For a biased molecular dynamics run with Andersen thermostat, one has to:
#Set the standard MD-related tags: {{TAG|IBRION}}=0, {{TAG|TEBEG}}, {{TAG|POTIM}}, and {{TAG|NSW}}
#Set {{TAG|MDALGO}}=1 ({{TAG|MDALGO}}=11 in VASP 5.x), and choose an appropriate setting for {{TAG|ANDERSEN_PROB}}
#In order to avoid updating of the bias potential, set {{TAG|HILLS_BIN}}={{TAG|NSW}}
#Define collective variables in the {{FILE|ICONST}}-file, and set the <tt>STATUS</tt> parameter for the collective variables to 5
#Define the bias potential in the {{FILE|PENALTYPOT}}-file
== Nose-Hoover thermostat ==
* For a biased molecular dynamics run with Nose-Hoover thermostat, one has to:
#Set the standard MD-related tags: {{TAG|IBRION}}=0, {{TAG|TEBEG}}, {{TAG|POTIM}}, and {{TAG|NSW}}
#Set {{TAG|MDALGO}}=2 ({{TAG|MDALGO}}=21 in VASP 5.x), and choose an appropriate setting for {{TAG|SMASS}}
#In order to avoid updating of the bias potential, set {{TAG|HILLS_BIN}}={{TAG|NSW}}
#Define collective variables in the {{FILE|ICONST}}-file, and set the <tt>STATUS</tt> parameter for the collective variables to 5
#Define the bias potential in the {{FILE|PENALTYPOT}}-file


The values of all collective variables for each MD step are listed in the {{FILE|REPORT}}-file, check the lines after the string <tt>Metadynamics</tt>.
One of the most popular methods using bias potentials is umbrella sampling{{cite|torrie:jcp:1977}}. This method uses a bias potential to enhance sampling of &xi; in regions with low ''P''(&xi;<sub>''i''</sub>), e.g., transition regions of chemical reactions. The correct distributions are recovered afterward using the equation for <math>\langle A \rangle_{H}</math> above.


== References ==
== References ==
<references>
<references/>
<ref name="Torrie77">[http://dx.doi.org/10.1016/0021-9991(77)90121-8 G. M. Torrie and J. P. Valleau, J. Comp. Phys. 23, 187 (1977).]</ref>
<ref name="FrenkelSmit">D. Frenkel and B. Smit, ''Understanding molecular simulations: from algorithms to applications'', Academic Press: San Diego, 2002.</ref>
</references>
----


[[Category:Molecular dynamics]][[Category:Biased molecular dynamics]][[Category:Theory]][[Category:Howto]]
[[Category:Advanced molecular-dynamics sampling]][[Category:Theory]]

Latest revision as of 11:40, 16 October 2024

Biased molecular dynamics' (MD) refers to advanced MD-simulation methods that introduce a bias potential. One of the most important purposes of using bias potentials is to enhance the sampling of phase space with low probability density (e.g., transition regions of chemical reactions). Depending on the type of sampling and in combination with the corresponding statistical methods one then has access to important thermodynamic quantities like, e.g., free energies. Biased molecular dynamics comes in very different flavors such as, e.g., umbrella sampling[1] and umbrella integration[2], to name a few. For a comprehensive description (especially about umbrella sampling), we refer the interested user to Ref. [3] written by D. Frenkel and B. Smit.

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 on one or multiple selected internal coordinates of the system ξ=ξ(q), the Hamiltonian takes the 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

More generally, an observable

can be expressed in terms of thermal averages within the biased ensemble as

One of the most popular methods using bias potentials is umbrella sampling[1]. This method uses a bias potential to enhance sampling of ξ in regions with low Pi), e.g., transition regions of chemical reactions. The correct distributions are recovered afterward using the equation for above.

References