schrodinger.application.matsci.mecp_mod module¶

Classes and functions for finding MECPs.

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 coordinates
jobs (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

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

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 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, 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 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
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 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)
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: 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.

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 write
properties (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