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
class schrodinger.application.matsci.mecp_mod.ParserWrapper(scriptname, description)

Bases: object

Manages the argparse module to parse user command line arguments.

__init__(scriptname, description)

Create a ParserWrapper instance and process it.

Parameters

  • scriptname (str) – name of this script

  • description (str) – description of this script

loadIt()

Load ParserWrapper with options.

parseArgs(args)

Parse the command line arguments.

Parameters

args (tuple) – command line arguments

exception schrodinger.application.matsci.mecp_mod.InputError

Bases: Exception

__init__(*args, **kwargs)
args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

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

static checkParallelization(tpp, nresources)

Check the parallelization options.

Parameters

  • tpp (int) – the number of threads to use for this Jaguar job, i.e. -TPP (threads-per-process)

  • nresources (int) – the number of resources for Jaguar calculations, i.e. the number of threads * the number of processors

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

__init__(*args, **kwargs)
args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

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', host='localhost', nproc=2, 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', host='localhost', nproc=2, 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

  • host (str) – the host on which to run the jobs

  • nproc (int) – the number of processors to use for running the jobs, i.e. the number of simultaneous jobs

  • 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

queue.JobControlJob

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 jobs

  • 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

  • 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

__init__(*args, **kwargs)
args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

exception schrodinger.application.matsci.mecp_mod.IterationError

Bases: Exception

__init__(*args, **kwargs)
args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

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

__init__(*args, **kwargs)
args
with_traceback()

Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

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 direction pk

fcallable

Function f(x)

fprimecallable

Gradient of f

xkarray_like

Current point

pkarray_like

Search direction

gfkarray_like, optional

Gradient of f at point xk

old_fvalfloat, optional

Value of f at point xk

old_old_fvalfloat, optional

Value of f at point preceding xk

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 point alpha

phi0float

Value of phi at alpha=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, host='localhost', nproc=2, 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, host='localhost', nproc=2, 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 needed

  • data (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

  • host (str) – the host on which to run the jobs

  • nproc (int) – the number of processors to use for running the jobs, i.e. the number of simultaneous jobs

  • 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

setUpMaeWriter()

Set up the Maestro output writer.

tearDownMaeWriter(exception)

Tear down the Maestro output writer and then raise an exception.

Parameters

exception (Exception) – the exception to raise after tear down

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, nresources=2, 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, nresources=2, verbose=False, logger=None)

Create an instance.

Parameters

  • astructure (schrodinger.structure.Structure) – the structure for which the MECP is needed

  • charge_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)

  • nresources (int) – the number of resources to use for Jaguar calculations, i.e. the number of threads * the number of processors

  • 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.

setParallelization()

Set the parallelization options.

setUpMaeWriter()

Set up the Maestro output writer.

tearDownMaeWriter(exception=None)

Tear down the Maestro output writer and then raise an exception if there is one.

Parameters

exception (Exception or None) – the exception to raise after tear down or None if there isn’t one

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

BFGS

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

EnergyAndGradients

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.

doIncorporation()

Incorporate output Maestro files.

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_and_write_structure(astructure, properties, writer)

Set the given properties on the structure and write it using the given writer.

Parameters
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