Genetic Algorithm Machine Learning (GAML)
Genetic Algorithm Machine Learning (GAML) software package for automated force field parameterization.
Xiang Zhong and Orlando Acevedo*, University of Miami
This machine learning based software package automates the creation of force field (FF) parameters for molecular dynamics (MD) or Monte Carlo (MC) simulations. In the current build, atomic charge development is emphasized for solvent simulations using a genetic algorithm crossover/average/mutation method. GAML outputs GROMACS formatted files in the OPLS-AA formalism for use in MD simulations. The FF parameters are validated by default against user-supplied free energies of hydration (ΔGhyd), liquid densities, and heats of vaporization (ΔHvap). However, additional condensed phased physical properties are available (or under development) for training that include: heat capacity, viscosity, self-diffusivity, dipoles, surface tension, and solubility.
Requirements
Download
git clone git://github.com/orlandoacevedo/GAML.git
Installation
pip[3] install gaml
Or using source codes
python[3] setup.py install
Usage
For helpful information, use
gaml
Or
gaml -h
Or, for sub-commands
gaml [command] -h
Option 1, use settingfile.txt
Parameters comments
=========================================== =====================================
command = charge_gen_range # command to execute, required
charge_path = BPYR_BF4_charge_collection.txt # input file path, required
atomnm = 24 # the processed atom number, required
percent = 0.8 # optional, default is 0.8
stepsize = 0.01 # optional, default is 0.01
nmround = 3 # optional, default is 3
fname = ChargeGenRange # optional, default is ChargeGenRange
The templates for the settingfile.txt can be found in the sample/ directory.
Option 2, use the command line
Usage:
gaml
charge_gen_range
charge_gen_scheme
file_gen_gaussian
file_gen_gromacstop
file_gen_mdpotential
file_gen_scripts
fss_analysis
GAML
GAML_autotrain
> gaml charge_gen_range
-f, --charge_path input charge file path
-i, --atomnm total atom numbers of single system
-p, --percent range from 0.0 ~ 1.0, default is 0.8
-t, --stepsize default is 0.01
-nr, --nmround decimal round-off number, default is 3
-o, --fname output file name, default is ChargeRange
> gaml charge_gen_scheme
-f, --charge_path input charge file
-sl, --symmetry_list list contains atom's chemical equivalent, index starting from 1
-ol, --offset_list two offsets to fit charge constrain
--offset_nm loop numbers to for offsets
--cl, --counter_list force total charges in this group to zero
-tc, --total_charge default is 1.0
-nz, --bool_nozero force no zero charges was generated, default is True
-nu, --bool_neutral force final calculated value scaled from 1 or not, default is True
-q, --bool_limit force charge sign, either positive or negative, default is None
-nr, --nmround decimal round number, default is 2
-b, --in_keyowrd the mark of start in the input file
-nm, --gennm output file numbers, default is 5
-lim, --threshold threshold for the charge value generation
-o, --fname output file name, default is ChargeRandomGen
> gaml file_gen_gaussian
-ftop, --toppath GROMACS topology file
-f, --file_path GROMACS output pdb/gro file
-sr, --select_range Angstrom, default is 10
-bs, --basis_set Gaussian definition, default is # HF/6-31G(d) Pop=CHelpG
-cs, --charge_spin Gaussian definition, default is 0 1
-nm, --gennm output file numbers, default is 5
-o, --fname output file name, default is GaussInput
> gaml file_gen_gromacstop
-f, --charge_path input charge file
-ftop, --toppath GROMACS topology file
-sl, --symmetry_list a python type list contains atom's chemical equivalent
-res, --reschoose choose residue, default is ALL,
-b, --in_keyowrd the mark of start in the input file
-e, --cut_keyowrd the mark of end in the input file
-nm, --gennm output file numbers, default is 5
-o, --fname output file name, default is GromacsTopfile
> gaml GAML
-f, --file_path input MD file
-fc, --charge_path input charge file
-sl, --symmetry_list list contains atom's chemical equivalent, index starting from 1
-ol, --offset_list two offsets to fit charge constrain
--offset_nm loop numbers to for offsets
--cl, --counter_list force total charges in this group to zero
-tc, --total_charge default is 0.0
-nz, --bool_nozero force no zero charges was generated, default is True
-nu, --bool_neutral force final calculated value scaled from 1 or not, default is True
-q, --bool_limit force charge sign, either positive or negative, default is None
-nr, --nmround decimal round number, default is 2
-nm, --gennm output file numbers, default is 5
-lim, --threshold threshold for the charge value generation
-d, --error_tolerance default is 0.8
-ex, --charge_extend_by the value to mutate charge range bound, default is 0.3
-ro, --ratio ratio among Cross-over to Average to Mutation. default is 7:2:1
-abs, --bool_abscomp use absolute value or not
-e, --cut_keyowrd the mark of end in the input file
-o, --fname output file name, default is ChargeGen
> gaml fss_analysis
-f, --file_path input analyzing file
-t, --stepsize default is 0.01
-d, --error_tolerance default is 0.28
-abs, --bool_abscomp default is False, use the absolute value or not
-p, --percent range from 0.0 ~ 1.0, default is 0.95
-e, --cut_keyword the mark of the end in the input file, default is MAE
-tl, --atomtype_list correspondent atom types, note the character '#' is not supported
-pn, --pallette_nm number of pallettes used to plot the graph, default is 50
-cm, --color_map compatible with Matplotlib modules, default is rainbow
-o, --fname output file name, default is FSSPlot
> file_gen_mdpotential
-f, --file_path FILE_PATH MD simulation result file
-s, --chargefile Input charge file
-lv, --literature_value correspondent literature value
-i, --atomnm total number of molecules in liquid phase, default is 500
--MAE mean-absolute-value, default is 0.05
--temperature unit in Kelvin
--block mark for file process, default is COUNT
--bool_gas gas phase calculation, default is False
-kw, --kwlist MD result keyword list, default is Density
-o, --fname output file name, default is MDProcess
> file_gen_scripts
-n, --number which script to choose, sequenced by -a
-a, --available show available built-in scripts
> GAML_autotrain
-f, --file_path auto training parameters all-in-one file
--bashinterfile user defined Bash interface file
Notes
A test for a 1-butylpyridinium-based ionic liquid can be found under the sample/ directory.
The OPLS-AA parameters for 86 conventional solvents optimized by GAML can be found under the Solvents/ directory. Files formatted for GROMACS.
Some features worth mentioning:
- Customized selection range for Coulombic interactions with PBC removal
- Two offsets as well as chemical equivalence considerations for random charge generation
- The crossover/average/mutation method
References
Zhong, X.; Velez, C.; Acevedo, O. "Partial Charges Optimized by Genetic Algorithms for Deep Eutectic Solvent Simulations" J. Chem. Theory Comput., 2021, 17, (in press). doi:10.1021/acs.jctc.1c00047
About
Contributing Authors: Xiang Zhong and Orlando Acevedo*
Funding: Gratitude is expressed to the National Science Foundation.
Software License: GAML. Genetic Algorithm Machine Learning (GAML) software package. Copyright (C) 2021 Orlando Acevedo
