Learning Atom-based Parameters

The equilibrium geometry parameters can be directly obtained
from a single coordinate set or averaged over
successive coordinate sets.
If just a single coordinate set is available, one can learn only
the equilibrium geometry, not the energy constants.
If an ensemble of coordinates is available,
energy constants can be derived assuming equipartition of energy
among the different internal coordinates. This is only approximately
verified in a real system, since there is actually coupling among
the internal coordinates.

The brackets represent an average over the ensemble of coordinate sets. is the mean thermal energy per harmonic degree of freedom at ; Eqs. 4.4, 4.5, and 4.6 define the other constants. The last expression assumes that all dihedral angles and torsion angles are represented by a harmonic functional form with periodicity set to zero (Eq. 4.6). In fact, the learn facility will set the periodicity of all “learned" dihedral and improper angles to zero. In the case that one of the variances in the denominators of Eq. 3.1 becomes zero, the corresponding energy constant is set to 999999.

Parameter learning is not possible for nonbonded and hydrogen-bonded parameters.

**learn-statement:==**-
**INITiate { learn-options }**- initializes selected parameters for the learning process.
**ACCUmulate**- includes data from the current main coordinate set in the running averages.
**TERMinate**- calculates final averages.

**learn-options:==**-
**SELEction=selection**- learns atom-based parameters for specified atoms. All atoms of a particular interaction term (bond, bond angle, dihedral angle, improper angle) have to be selected in order for the parameters for that term to be learned (default: (ALL) ).
**MODE=STATistics NOSTatistics**- specifies whether energy constants are to be learned using Eq. 3.1. The energy constants will be learned if STATistics is specified; otherwise the energy constants will be untouched (default: STATistics).

learn initiate selection=( name c* ) MODE=STATistics end learn accumulate end learn terminate end

- Requirements
- Example: Learning Unknown Equilibrium Parameters from Coordinates
- Example: Learning Atom-based Parameters from an Ensemble of Structures