ML LCOUPLE: Difference between revisions

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{{TAGDEF|ML_FF_LCOUPLE_MB|[logical]|.FALSE.}}
{{TAGDEF|ML_FF_LCOUPLE_MB|[logical]|.FALSE.}}


Description: This tag specifies whether coupling parameters are used for the calculation of chemical potentials is used or not within the machine learning force field method.
Description: This tag specifies whether coupling parameters are used for the calculation of chemical potentials or not within the machine learning force field method.
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Revision as of 09:21, 6 July 2021

ML_FF_LCOUPLE_MB = [logical]
Default: ML_FF_LCOUPLE_MB = .FALSE. 

Description: This tag specifies whether coupling parameters are used for the calculation of chemical potentials or not within the machine learning force field method.


In thermodynamic integration a coupling parameter is introduced to the Hamiltonian to smoothly switch between a "non-interacting" reference state and a "fully-interacting" state. The change of the free energy along this path is written as

Using machine learning force fields the Hamiltonian can be written as

where denotes the number of atoms and is an atomic reference energy for a single non interacting atom. The first term in the equation describes the potential energy and the second and third term describe the potential energy of an atom . The index denotes the atoms whose interaction is controlled by a coupling parameter. The interaction of the atoms are controlled by scaling the contributions to the atom density via the coupling parameter


Further details on the implementation can be found in reference [1].

For thermodynamic integration the following parameters have to be set:

The derivative of the hamiltonian with respect to the coupling constant is written out at every MD step to the ML_LOGFILE. A sample output should look like the following

dH/dRCOUPLE (eV):     0.893558

References


Related Tags and Sections

ML_FF_LMLFF, ML_FF_NATOM_COUPLED_MB, ML_FF_ICOUPLE_MB, ML_FF_RCOUPLE_MB

Examples that use this tag