Page Comparison

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CH4 ['a2', 'b83', 'n16', 'n2']
C2H6 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't3']
C3H8 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't3', 't4']
C4H10 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C5H12 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C6H14 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C7H16 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']

C3H6 ['a3', 'a4', 'a6', 'b1', 'b83', 'n16', 'n2', 't15', 't16']
C4H8 ['a2', 'a7', 'a9', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C5H10 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C6H12 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C7H14 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C8H16 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']
C9H18 ['a1', 'a2', 'b1', 'b83', 'n16', 'n2', 't2', 't3', 't4']

Another subset with just a1(C-C-C) matching molecules

Removing CH4, C3H6, C4H8 from the set and with rest of the molecules that match to a1

Test fits with sulfonamide (optimizing only a30, a31)

Targets

Torsion drives, opt geo, and vib freq targets that match to a30, a31 from release-1.2.0 targets

With Replacing a30 and a31 in openff_unconstrained-1.3.0 with Frostt99 parameters (as starting point) and running the following fits

1. refit using all the targets (td + opt + vib freq)

2. refit with only opt_geo targets

3. refit with only vib_freq targets

4. refit with only td targets

5. refit with td+opt_geo targets

6. test fit with a child parameter for a30

   1. a30b *=[#16X4]=* initial value for angle parameter as 125 degrees, and k same as a30 (140)

Fits in progress, most of them crossed five iterations and obj. function values are converging, parameter values at the last iteration as of now (will update after the runs complete)

completed

*angle in degrees, k in kcal/mol/rad^2

Comparison of opt only targets' fits with change in forcebalance options

L2 penalty on the objective function for regularization.

penalty_additive: Factor for additive penalty function in objective function. Add a penalty to the objective function (e.g. L2 or L1 norm) with this prefactor. Using an additive penalty requires an assessment of the order of magnitude of the objective function, but it is closer to the
statistical concept of ridge or LASSO regression.

penalty_multiplicative: Factor for multiplicative penalty function in objective function.
Multiply the objective function by (1+X) where X is this value. Using an multiplicative penalty
works well for objective functions of any size but it is not equivalent to statistical regularization methods.

use_pvals: Bypass the transformation matrix (mvals) and use the physical parameters directly