schrodinger.application.matsci.mecp_mod module¶
Classes and functions for finding MECPs.
Copyright Schrodinger, LLC. All rights reserved.
- schrodinger.application.matsci.mecp_mod.rosen(x_vec)¶
The Rosenbrock function. The target solution should be all ones and the function value here should be zero.
- Parameters
x_vec (numpy.array) – the solution vector (N X 1)
- Return type
float, numpy.array
- Returns
the function value and Jacobian (N X 1)
- schrodinger.application.matsci.mecp_mod.quadratic_1(x_vec)¶
The x**2 + y**2 + 2*x function. The target solution is (x = -1, y = 0) with f = -1. The minimum crossing point with quadratic_2 is (x = -1/2, y = -1/2).
- Parameters
x_vec (numpy.array) – the solution vector (N X 1)
- Return type
float, numpy.array
- Returns
the function value and Jacobian (N X 1)
- schrodinger.application.matsci.mecp_mod.quadratic_2(x_vec)¶
The x**2 + y**2 + 2*y function. The target solution is (x = 0, y = -1) with f = -1. The minimum crossing point with quadratic_1 is (x = -1/2, y = -1/2).
- Parameters
x_vec (numpy.array) – the solution vector (N X 1)
- Return type
float, numpy.array
- Returns
the function value and Jacobian (N X 1)
- schrodinger.application.matsci.mecp_mod.quadratic_3(x_vec)¶
The x**2 + y**2 + 6*x function. The target solution is (x = -3, y = 0) with f = -9. The minimum crossing point with quadratic_2 is (x = -3/10, y = -9/10).
- Parameters
x_vec (numpy.array) – the solution vector (N X 1)
- Return type
float, numpy.array
- Returns
the function value and Jacobian (N X 1)
- schrodinger.application.matsci.mecp_mod.get_guess(nvar=33, amp=5, seed=123)¶
Return a guess solution.
- Parameters
nvar (int) – the number of variables
amp (int) – the amplitude of the set of random numbers used in generating the guess solution
seed (int) – seed for random
- Return type
numpy.array
- Returns
the guess geometry (N X 1)
- schrodinger.application.matsci.mecp_mod.set_e_and_g(astructure, jobs)¶
Set the energies and gradients for a debug run.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure containing the solution as coordinatesjobs (list) – contains instances of JaguarJob
- exception schrodinger.application.matsci.mecp_mod.InputError¶
Bases:
Exception
- class schrodinger.application.matsci.mecp_mod.CheckInput¶
Bases:
object
Manages checking input.
- static checkChargesMultiplicitiesStates(charge_1, charge_2, multip_1, multip_2, state_1, state_2)¶
Check the charges, multiplicities, and states.
- Parameters
charge_1 (int) – the charge of the first state
charge_2 (int) – the charge of the second state
multip_1 (int) – the multiplicity of the first state
multip_2 (int) – the multiplicity of the second state
state_1 (int) – the first state, 0 for ground state, N for the N-th excited state
state_2 (int) – the second state, 0 for ground state, N for the N-th excited state
- Raise
InputError if there is an issue
- schrodinger.application.matsci.mecp_mod.get_final_state(state, scf_gs, multiplicity)¶
Return the final state.
- Parameters
state (int) – the electronic state, 0 is the ground state and <N> is the N-th excited state
scf_gs (bool) – specify whether ground states should be determined using an SCF
multiplicity (int) – molecular multiplicity
- Return type
int or None
- Returns
the final state or None if there isn’t one
- class schrodinger.application.matsci.mecp_mod.JaguarJob(idx=None, base_name=None, charge=None, multiplicity=None, state=None, scf_gs=None, kwargs=None, mae_input_file=None, jag_input_file=None, jag_output_file=None, jag_restart_input_file=None, in_template=None, job=None)¶
Bases:
object
Manages a Jaguar job.
- KEYS = ['idx', 'base_name', 'charge', 'multiplicity', 'state', 'scf_gs', 'kwargs', 'mae_input_file', 'jag_input_file', 'jag_output_file', 'jag_restart_input_file', 'in_template', 'job']¶
- __init__(idx=None, base_name=None, charge=None, multiplicity=None, state=None, scf_gs=None, kwargs=None, mae_input_file=None, jag_input_file=None, jag_output_file=None, jag_restart_input_file=None, in_template=None, job=None)¶
Create an instance.
- Parameters
idx (int) – the index
base_name (str) – the base name
charge (int) – net molecular charge
multiplicity (int) – molecular multiplicity
state (int) – the electronic state, 0 is the ground state and <N> is the N-th excited state
scf_gs (bool) – specify whether ground states should be determined using an SCF
kwargs (dict) – dictionary of Jaguar &gen section key-value pairs
mae_input_file (str) – Maestro input file
jag_input_file (str) – Jaguar input file
jag_output_file (str) – Jaguar output file
jag_restart_input_file (str) – Jaguar restart input file
in_template (str) – a Jaguar input file to use as template
job (queue.JobControlJob) – job object
- getFinalTotalEnergy()¶
Return the final total energy accounting for any excitation energies.
- Return type
float
- Returns
the total energy in hartree
- getFinalForcesHartreePerAngstrom()¶
Return the final forces in units of hartree/angstrom.
- Return type
numpy.array
- Returns
the forces in hartree/angstrom in natoms X 3
- getFinalStructure()¶
Return the final structure.
- Parameters
astructure (
schrodinger.structure.Structure
) – the final structure
- exception schrodinger.application.matsci.mecp_mod.JaguarError¶
Bases:
Exception
- class schrodinger.application.matsci.mecp_mod.JaguarJobs(data=[(0, 3, 0), (0, 1, 1)], scf_gs=False, kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, base_name='mecp', tpp=1, logger=None)¶
Bases:
object
Manages Jaguar jobs.
- EXTRA_NROOT = 4¶
- __init__(data=[(0, 3, 0), (0, 1, 1)], scf_gs=False, kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, base_name='mecp', tpp=1, logger=None)¶
Create an instance.
- Parameters
data (list) – contains (charge, multiplicity, state) tuples for all jobs, for electronic state 0 is the ground state and <N> is the N-th excited state
scf_gs (bool) – specify whether ground states should be determined using an SCF
kwargs (dict) – dictionary of Jaguar &gen section key-value pairs
base_name (str) – a base name used to name the jobs and their related files
tpp (int) – the number of threads to use for the jobs, i.e. -TPP (threads-per-process)
logger (logging.Logger or None) – output logger or None if there isn’t one
- clearJobs()¶
Clear the list of jobs attribute.
- setStructure(astructure)¶
Set the structure to use for the jobs.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure to use for the jobs
- setInTemplates(in_templates=None)¶
Set the input templates to use for the jobs.
- Parameters
in_templates (list or None) – a list of Jaguar input files to use as templates for creating the jobs, if used then the length of this list should be equivalent to that of the data attribute, the first ZMAT section is overwritten with the input astructure argument, use None if there aren’t any templates
- Raise
JaguarError if in_templates data is the wrong size
- setUpLaunchDir(launch_dir=None)¶
Set up the launch directory for the jobs.
- Parameters
launch_dir (str or None) – the directory from which to launch the jobs or None if there isn’t one (in which case the CWD will be used)
- getBaseName(idx)¶
Return a base name for the given job.
- Parameters
idx (int) – the job index
- Return type
str
- Returns
base name for the job
- getKwargs(charge, multiplicity, state)¶
Return the kwargs for the given job.
- Parameters
charge (int) – net molecular charge
multiplicity (int) – molecular multiplicity
state (int) – the electronic state, 0 is the ground state and <N> is the N-th excited state
- Return type
dict
- Returns
Jaguar kwargs for the job
- getFileNames(base_name)¶
Return a list of files names for the given job.
- Parameters
base_name (str) – base name for the job
- Return type
list
- Returns
file names for the job
- getInTemplate(idx)¶
Return an input template file.
- Parameters
idx (int) – the job index
- Return type
str
- Returns
the input template file
- getJob(base_name, jag_input_file)¶
Return a queue.JobControlJob for the given job.
- Parameters
base_name (str) – base name for the job
jag_input_file (str) – Jaguar input file for the job
- Return type
- Returns
the job object
- setUpInputFiles(job)¶
Set up the input files for the job.
- Parameters
job (JaguarJob) – a JaguarJob object containing various job parameters
- runJobs()¶
Run the jobs.
- processOutput()¶
Process the output from the jobs.
- Raise
JaguarError if there were problems with the job
- runIt(astructure, in_templates=None, launch_dir=None)¶
Main function to run the jobs.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure to use for the jobsin_templates (list or None) – a list of Jaguar input files to use as templates for creating the jobs, if used then the length of this list should be equivalent to that of the data attribute, the first ZMAT section is overwritten with the input astructure argument, use None if there aren’t any templates
launch_dir (str or None) – the directory from which to launch the jobs or None if there isn’t one (in which case the CWD will be used)
- Return type
list
- Returns
contains JaguarJob objects for all jobs
- exception schrodinger.application.matsci.mecp_mod.TerminateException¶
Bases:
Exception
- exception schrodinger.application.matsci.mecp_mod.IterationError¶
Bases:
Exception
- class schrodinger.application.matsci.mecp_mod.MECPStep(iteration, call_number, x_initial, method, perp_factor, para_factor, verbose, logger, bfgs_obj)¶
Bases:
object
Manage an MECP step, which can be an iteration or a line search iteration.
- ALPHA_THRESH = 1e-10¶
- __init__(iteration, call_number, x_initial, method, perp_factor, para_factor, verbose, logger, bfgs_obj)¶
Create an instance.
- Parameters
iteration (int) – the iteration
call_number (int) – the call number
x_initial (numpy.array) – the initial geometry vector (N X 1)
method (str) – the method to use to determine the MECP
perp_factor (float) – prefactor for the energy term whose gradient lies perpendicular to the crossing seam
para_factor (float) – prefactor for the energy term whose gradient lies parallel to the crossing seam
verbose (bool) – specifies verbose logging
logger (logging.Logger) – output logger
bfgs_obj (BFGS) – a BFGS object that manages the optimization
- logInitialGeometry()¶
Log the initial geometry.
- logHessianEigVals()¶
Log the Hessian eigenvalues.
- logStepInfo()¶
Log the step info.
- handleFinalGeometry(x_final)¶
Handle the final geometry.
- Parameters
x_final (numpy.array) – the final geometry vector (N X 1)
- setInTemplates()¶
Set the Jaguar input templates.
- getUnitNormalToDeltaForces(prev_mecp_step=None)¶
Get the unit vector that is normal to the delta forces vector.
- Parameters
prev_mecp_step (MECPStep or None) – MECPStep from the previous iteration or line search iteration or None if there isn’t one
- Return type
numpy.array or None
- Returns
the unit vector perpendicular to the delta forces vector or None if there isn’t one
- setEnergiesAndForces(prev_mecp_step=None)¶
Set the energies and forces for the two states as well as well as the energy and forces to use for the MECP optimization.
- Parameters
prev_mecp_step (MECPStep or None) – MECPStep from the previous iteration or line search iteration or None if there isn’t one
- setEnergiesAndForcesData(prev_mecp_step=None)¶
Set energies and forces data.
- Parameters
prev_mecp_step (MECPStep or None) – MECPStep from the previous iteration or line search iteration or None if there isn’t one
- setJobData(jobs, prev_mecp_step=None)¶
Set job data.
- Parameters
jobs (list) – contains the JaguarJob instances for the two jobs
prev_mecp_step (MECPStep or None) – MECPStep from the previous iteration or line search iteration or None if there isn’t one
- logEnergyAndForces(header, energy, forces, max_force, rms_force, len_force, energy_header='Energy / Hartree', forces_header='Forces / Hartree/Ang.')¶
Log energy and forces.
- Parameters
header (str) – a header
energy (float or None) – the energy or None if there isn’t one
forces (numpy.array) – the forces (natoms X 3)
max_force (float) – the magnitude of the largest forces element
rms_force (float) – the RMS of forces
len_force (float) – the length of forces
energy_header (str) – an energy header
forces_header (str) – a forces header
- getFormattedSummaryLine(entries)¶
Return a formatted summary line from the given data entries.
- Parameters
entries (list) – the data to format
- Return type
str
- Returns
a formatted summary line
- logSummaryHeader()¶
Log the summary header.
- logSummary()¶
Log a summary.
- logJobData()¶
Log job data.
- terminate(convergence_dict)¶
Terminate the MECP optimization.
- Parameters
convergence_dict (dict) – contains various convergence thresholds
- Raise
TerminateException if it is time to terminate
- exception schrodinger.application.matsci.mecp_mod.MinimizerError¶
Bases:
Exception
- class schrodinger.application.matsci.mecp_mod.BFGS(c1=0.0001, c2=0.9, amax=50.0, amin=1e-08, xtol=1e-14, max_force=0.0001, max_iterations=50, eps=0.0001, init_hess='identity', verbose=False, logger=None)¶
Bases:
object
Manage a BFGS optimization.
- LARGE_RHO_K = 1000.0¶
- IMAG_TOL = 1e-06¶
- ANGLE_TOL = 0.01¶
- NEG_HESS_TOL = -0.01¶
- __init__(c1=0.0001, c2=0.9, amax=50.0, amin=1e-08, xtol=1e-14, max_force=0.0001, max_iterations=50, eps=0.0001, init_hess='identity', verbose=False, logger=None)¶
Create an instance.
- Parameters
c1 (float) – parameter for Armijo condition rule
c2 (float) – parameter for curvature condition rule
amax (float) – maximum allowable step size
amin (float) – minimum allowable step size
xtol (float) – nonnegative relative tolerance for an acceptable step, the search exits with a warning if the relative difference between sty and stx is less than xtol where sty and stx define an interval
max_force (float) – maximum allowable force element
max_iterations (int) – the maximum number of iterations
eps (float) – step size in Angstrom for any finite difference approximations
init_hess (str) – the type of initial Hessian to use
verbose (bool) – specifies verbose logging
logger (logging.Logger or None) – output logger or None if there isn’t one
- line_search_wolfe1(f, fprime, xk, pk, gfk=None, old_fval=None, old_old_fval=None, args=(), c1=0.0001, c2=0.9, amax=50, amin=1e-08, xtol=1e-14)¶
As
scalar_search_wolfe1
but do a line search to directionpk
- fcallable
Function
f(x)
- fprimecallable
Gradient of
f
- xkarray_like
Current point
- pkarray_like
Search direction
- gfkarray_like, optional
Gradient of
f
at pointxk
- old_fvalfloat, optional
Value of
f
at pointxk
- old_old_fvalfloat, optional
Value of
f
at point precedingxk
The rest of the parameters are the same as for
scalar_search_wolfe1
.- stp, f_count, g_count, fval, old_fval
As in
line_search_wolfe1
- gvalarray
Gradient of
f
at the final point
- scalar_search_wolfe1(phi, derphi, phi0=None, old_phi0=None, derphi0=None, c1=0.0001, c2=0.9, amax=50, amin=1e-08, xtol=1e-14)¶
Scalar function search for alpha that satisfies strong Wolfe conditions
alpha > 0 is assumed to be a descent direction.
- phicallable phi(alpha)
Function at point
alpha
- derphicallable dphi(alpha)
Derivative
d phi(alpha)/ds
. Returns a scalar.- phi0float, optional
Value of
f
at 0- old_phi0float, optional
Value of
f
at the previous point- derphi0float, optional
Value
derphi
at 0- amaxfloat, optional
Maximum step size
- c1, c2float, optional
Wolfe parameters
- alphafloat
Step size, or None if no suitable step was found
- phifloat
Value of
phi
at the new pointalpha
- phi0float
Value of
phi
atalpha=0
Uses routine DCSRCH from MINPACK.
- resetFiniteDiffCall()¶
Reset the finite difference call.
- getInitialInvHessian(fun, jac, fun_0, jac_0, x_0)¶
Return an initial guess for the inverse Hessian.
- Parameters
fun (function) – function to minimize
jac (function) – the Jacobian of the function being minimized
fun_0 (float) – function value at initial solution
jac_0 (float) – the Jacobian value at initial solution
x_0 (numpy.array) – initial solution
- Return type
numpy.array
- Returns
the initial guess inverse Hessian (N/3 X 3)
- minimize(fun, x_0, jac=None, **kwargs)¶
Minimization of a function using the BFGS algorithm.
- Parameters
fun (function) – function to minimize
x_0 (numpy.array) – initial solution
jac (function) – the Jacobian of the function being minimized
- Return type
scipy.optimize.optimize.OptimizeResult
- Returns
optimization parameters
- class schrodinger.application.matsci.mecp_mod.EnergyAndGradients(astructure, data=[(0, 3, 0), (0, 1, 1)], scf_gs=False, kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, base_name='mecp', all_results=False, convergence_dict={'delta_energy': - 1e-06, 'max_displacement': 1e-05, 'max_force': 0.0001, 'rms_displacement': 1e-05, 'rms_force': 0.0001}, max_iterations=50, method='projection', perp_factor=100.0, para_factor=1.0, all_geometries=False, tpp=1, verbose=False, logger=None, bfgs_obj=None)¶
Bases:
object
Manage energy and gradient calls.
- __init__(astructure, data=[(0, 3, 0), (0, 1, 1)], scf_gs=False, kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, base_name='mecp', all_results=False, convergence_dict={'delta_energy': - 1e-06, 'max_displacement': 1e-05, 'max_force': 0.0001, 'rms_displacement': 1e-05, 'rms_force': 0.0001}, max_iterations=50, method='projection', perp_factor=100.0, para_factor=1.0, all_geometries=False, tpp=1, verbose=False, logger=None, bfgs_obj=None)¶
Create an instance.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure for which the energy and gradients are neededdata (list) – contains (charge, multiplicity, state) tuples for the jobs for the two MECP states, for electronic state 0 is the ground state and <N> is the N-th excited state
scf_gs (bool) – specify whether ground states should be determined using an SCF
kwargs (dict) – dictionary of Jaguar &gen section key-value pairs
base_name (str) – a base name used to name the jobs and their related files
all_results (bool) – use this option to copy all subdirectories containing results from intermediate Jaguar force, etc. calculations back to the launch host
convergence_dict (dict) – contains various convergence thresholds
max_iterations (int) – the maximum number of MECP geometry optimization iterations
method (str) – the method to use to determine the MECP
perp_factor (float) – prefactor for the energy term whose gradient lies perpendicular to the crossing seam
para_factor (float) – prefactor for the energy term whose gradient lies parallel to the crossing seam
all_geometries (bool) – use this option to report all geometries, i.e. the geometries from all MECP geometries iterations will be reported in the output
tpp (int) – the number of threads to use for the jobs, i.e. -TPP (threads-per-process)
verbose (bool) – specifies verbose logging
logger (logging.Logger or None) – output logger or None if there isn’t one
bfgs_obj (BFGS) – a BFGS object that manages the optimization
- resetCallNumber()¶
Reset the call number.
- setUpJaguarJobs()¶
Set up for the Jaguar jobs.
- logHeader()¶
Log a header.
- newIteration()¶
Return True if this is a new iteration.
- Return type
bool
- Returns
True if this is a new iteration, False otherwise
- setUpIterationLaunchDir()¶
Set up the launch directory for this MECP iteration.
- Return type
str
- Returns
the launch directory for this MECP iteration
- getLaunchSubDir()¶
Return the name of the launch subdirectory for the current job.
- Return type
str
- Returns
the launch subdirectory for the current job
- runJobs(launch_dir)¶
Run the jobs.
- Parameters
launch_dir (str) – the launch directory for the jobs
- Return type
list
- Returns
contains the JaguarJob instances for the two jobs
- logSummary()¶
Log a summary.
- handleStructure(x_to_use, step=None)¶
Handle setting and writing the structure.
- Parameters
x_to_use (numpy.array) – the geometry vector to use (N X 1)
step (MECPStep or None) – the step from which to obtain the energy to use or None if there is no energy
- finishPrevStep(next_x)¶
Finish the previous step.
- Parameters
next_x (numpy.array) – the next geometry vector (N X 1)
- handleFiniteDifferences(next_x, finite_diff_call)¶
Handle finite difference calls.
- Parameters
next_x (numpy.array) – the next geometry vector (N X 1)
finite_diff_call (int) – the index of a finite difference call
- Return type
float, numpy.array
- Returns
the energy and gradients (N X 1)
- getEnergyAndGradients(next_x)¶
Return the energy and gradients that drive the MECP geometry optimization.
- Parameters
next_x (numpy.array) – the next geometry vector (N X 1)
- Return type
float, numpy.array
- Returns
the energy and gradients (N X 1)
- class schrodinger.application.matsci.mecp_mod.MECP(astructure, charge_1=0, charge_2=0, multip_1=3, multip_2=1, state_1=0, state_2=1, scf_gs=False, jaguar_kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, iterations=50, eps=0.0001, init_hess='identity', method='projection', perp_factor=100.0, para_factor=1.0, convergence_dict={'delta_energy': - 1e-06, 'max_displacement': 1e-05, 'max_force': 0.0001, 'rms_displacement': 1e-05, 'rms_force': 0.0001}, guess_geom='input', all_geometries=False, all_results=False, properties=False, base_name='mecp', tpp=1, verbose=False, logger=None)¶
Bases:
object
Manages finding MECPs.
- __init__(astructure, charge_1=0, charge_2=0, multip_1=3, multip_2=1, state_1=0, state_2=1, scf_gs=False, jaguar_kwargs={'basis': 'MIDIX', 'dftname': 'B3LYP', 'iacc': 1, 'isymm': 0, 'itda': 1, 'itddft': 1, 'iuhf': 2, 'maxit': 100, 'nofail': 0, 'nops': 1}, iterations=50, eps=0.0001, init_hess='identity', method='projection', perp_factor=100.0, para_factor=1.0, convergence_dict={'delta_energy': - 1e-06, 'max_displacement': 1e-05, 'max_force': 0.0001, 'rms_displacement': 1e-05, 'rms_force': 0.0001}, guess_geom='input', all_geometries=False, all_results=False, properties=False, base_name='mecp', tpp=1, verbose=False, logger=None)¶
Create an instance.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure for which the MECP is neededcharge_1 (int) – net molecular charge of the first state
charge_2 (int) – net molecular charge of the second state
multip_1 (int) – multiplicity of the first state
multip_2 (int) – multiplicity of the second state
state_1 (int) – the first state, 0 for ground state, N for the N-th excited state
state_2 (int) – the second state, 0 for ground state, N for the N-th excited state
scf_gs (bool) – specify whether ground states should be determined using an SCF
jaguar_kwargs (dict) – dictionary of Jaguar &gen section key/value pairs
iterations (int) – the maximum number of MECP geometry optimization iterations
eps (float) – step size in Angstrom for any finite difference approximations
init_hess (str) – the type of initial Hessian to use
method (str) – the method to use to determine the MECP
perp_factor (float) – prefactor for the energy term whose gradient lies perpendicular to the crossing seam
para_factor (float) – prefactor for the energy term whose gradient lies parallel to the crossing seam
convergence_dict (dict) – contains various convergence thresholds
guess_geom (str) – the initial guess geometry to use for the MECP geometry optimization, choices are GUESS_GEOM_CHOICES
all_geometries (bool) – use this option to report all geometries, i.e. the geometries from all MECP geometries iterations will be reported in the output
all_results (bool) – use this option to copy all subdirectories containing results from intermediate Jaguar force, etc. calculations back to the launch host
properties (bool) – whether to calculate properties of the MECP or not
base_name (str) – a base name used to name the job and its related files
tpp (int) – the number of threads to use for any intermediate Jaguar calculations, i.e. -TPP (threads-per-process)
verbose (bool) – specifies verbose logging
logger (logging.Logger or None) – output logger or None if there isn’t one
- setSCFGS()¶
Set the SCF ground state.
- setJaguarKwargs()¶
Set the Jaguar kwargs.
- checkInput()¶
Check input.
- logParams()¶
Log the parameters.
- doStateOptimizations()¶
Do the geometry optimizations for the individual states and record their data.
- getGuessGeometry()¶
Return the guess geometry.
- Return type
numpy.array
- Returns
the guess geometry (N X 1)
- postProcessOptimization(energy_and_gradients)¶
Post process the optimization.
- Parameters
energy_and_gradients (EnergyAndGradients) – the instance passed to the optimizer
- getBFGSObject(x_0)¶
Return a BFGS object.
- Parameters
x_0 (numpy.array) – the guess geometry (N X 1)
- Return type
- Returns
the object to manage the BFGS optimization
- getEnergyAndGradientsObject(bfgs_obj)¶
Return a EnergyAndGradients object.
- Parameters
bfgs_obj (BFGS) – the object to manage the BFGS optimization
- Return type
- Returns
the object to manage the energy and gradient calls for the BFGS optimization
- getOptKwargs(bfgs_obj)¶
Return the kwargs for the optimization.
- Parameters
bfgs_obj (BFGS) – the object to manage the BFGS optimization
- Return type
dict
- Returns
the parameters for the optimization
- optimize()¶
Optimize the geometry to the MECP.
- writeEnergiesToCSV(csv_data)¶
Export the energy data to a CSV file for both states and every iteration.
- Parameters
csv_data (defaultdict(list)) – Dictionary where the keys represent states, and the values are lists of energies for each MECP step.
- runIt()¶
Main function to find the MECP.
- schrodinger.application.matsci.mecp_mod.vector_to_matrix(vector)¶
Convert vector to matrix.
- Parameters
vector (numpy.array) – N X 1 array
- Return type
numpy.array
- Returns
N/3 X 3 array
- schrodinger.application.matsci.mecp_mod.matrix_to_vector(matrix)¶
Convert matrix to vector.
- Parameters
matrix (numpy.array) – N/3 X 3 array
- Return type
numpy.array
- Returns
N X 1 array
- schrodinger.application.matsci.mecp_mod.get_max_and_rms_and_len(matrix)¶
Return the max, RMS, and length of the given matrix.
- Parameters
matrix (numpy.array) – the matrix
- Return type
float, float, float
- Returns
the max, RMS, and length of the given matrix
- schrodinger.application.matsci.mecp_mod.log_vector(vector, header=None, log_sums=False, logger=None)¶
Log or print the given vector in a natoms X 3 format.
- Parameters
vector (numpy.array) – the vector to log (N X 1)
header (str or None) – a header or None if there isn’t one
log_sums (bool) – whether to log the sums of x, y, and z over atoms
logger (logging.Logger or None) – output logger or None if there isn’t one
- schrodinger.application.matsci.mecp_mod.log_geometry(geometry, header='Geometry / Ang.', logger=None)¶
Log or print the geometry in a natoms X 3 format.
- Parameters
geometry (numpy.array) – the geometry to log (N X 1)
header (str) – a header
logger (logging.Logger or None) – output logger or None if there isn’t one
- schrodinger.application.matsci.mecp_mod.log_displacements(displacements, max_displacement, rms_displacement, len_displacement, header='Displacements / Ang.', logger=None)¶
Log or print the displacements in a natoms X 3 format.
- Parameters
displacements (numpy.array) – the displacements to log (N X 1)
max_displacement (float) – the magnitude of the largest displacements element
rms_displacement (float) – the RMS of displacements
len_displacement (float) – the length of displacements
header (str) – a header
logger (logging.Logger or None) – output logger or None if there isn’t one
- schrodinger.application.matsci.mecp_mod.log_energy_and_forces(energy, forces, max_force, rms_force, len_force, energy_header='Energy / Hartree', forces_header='Forces / Hartree/Ang.', logger=None)¶
Log or print the energy and forces in a natoms X 3 format.
- Parameters
energy (float or None) – the energy or None if there isn’t one
forces (numpy.array) – the forces to log (N X 1)
max_force (float) – the magnitude of the largest forces element
rms_force (float) – the RMS of forces
len_force (float) – the length of forces
energy_header (str) – an energy header
forces_header (str) – a forces header
logger (logging.Logger or None) – output logger or None if there isn’t one
- schrodinger.application.matsci.mecp_mod.reciprocal_bohr_to_angstrom(array)¶
Return the given array converted from units of reciprocal Bohr to reciprocal Angstrom.
- Parameters
array (numpy.array) – the array to convert
- Return type
numpy.array
- Returns
the converted array
- schrodinger.application.matsci.mecp_mod.ev_to_hartree(energy)¶
Return the given energy converted from eV to Hartree.
- Return type
float
- Returns
energy in Hartree
- schrodinger.application.matsci.mecp_mod.hartree_to_kcal_per_mol(in_hartree)¶
Return the given energy converted from Hartree to kcal/mol.
- Parameters
in_hartree (float) – energy in Hartree
- Return type
float
- Returns
energy in kcal/mol
- schrodinger.application.matsci.mecp_mod.set_properties(astructure, properties)¶
Set the given properties on the structure.
- Parameters
astructure (
schrodinger.structure.Structure
) – the structure to writeproperties (dict) – key/value pairs of properties to write
- schrodinger.application.matsci.mecp_mod.get_angle_in_degrees(vec_1, vec_2)¶
Return the angle in units of degrees between the given two vectors.
- Parameters
vec_1 (numpy.array) – the first vector
vec_2 (numpy.array) – the second vector
- Return type
float
- Returns
the angle in degrees