Insight II

7       Ampac/Mopac Module


The Ampac/Mopac module provides an interface to the public domain AMPAC and MOPAC programs. AMPAC and MOPAC are general purpose semiempirical molecular orbital packages for the study of chemical structures and reactions. The semiempirical Hamiltonians MNDO, MINDO/3, AM1, and PM3 are used in the electronic part of the calculation to obtain molecular orbitals, the heat of formation, and its derivatives with respect to molecular geometry. Using these results, AMPAC and MOPAC calculate the vibrational spectra, thermodynamics quantities, isotopic substitution effects, and force constants for molecules, radicals, ions, and polymers. For studying chemical reactions, a transition state location routine and two transition state optimizing routines are available.

While AMPAC and MOPAC call upon concepts in quantum theory and thermodynamics, and use relatively advanced mathematics, you need not be familiar with these specialized topics. The Ampac/Mopac module is designed with the non-theoretician in mind. The Insight II program allows you to set up complex AMPAC and MOPAC calculations with a minimal amount of effort, so that you can concentrate on the chemistry involved rather than the quantum and thermodynamic concepts.

Summary of Ampac/Mopac Capabilities

1.   Makes use of MNDO, MINDO/3, AM1, and PM3 Hamiltonians (PM3 is not supported in AMPAC)

2.   Can use Restricted Hartree-Fock (RHF) and Unrestricted Hartree-Fock (UHF) methods

3.   Extensive Configuration Interaction (C.I.)

a. Up to 100 configurations

b. Singlets, Doublets, Triplets, Quartets, Quintets, and Sextets.

c. Excited states

d. Geometry optimization, etc., on specified states

4.   Single Point Self-Consistent Field (SCF) calculation

5.   Geometry optimization

6.   Gradient minimization

7.   Transition state location

8.   Reaction path coordinate calculation

9.   Force constant calculation

10.   Normal coordinate analysis

11.   Transition dipole calculation

12.   Thermodynamic property calculation

13.   Localized orbitals

14.   Covalent bond orders

15.   Bond analysis into sigma and pi contributions

16.   One dimensional polymer calculation

17.   Dynamic Reaction Coordinate calculation (DRC)

18.   Intrinsic Reaction Coordinate calculation (IRC). IRC is not supported in AMPAC.

MOPAC 6.0, AMPAC 2.1, and Density 1.0 are distributed with Insight II as executables. These programs can handle up to 83 heavy atoms and 83 light atoms. To obtain source code and further documentation for these packages, please contact QCPE via e-mail:; or write to them at: QCPE, Creative Arts Building 181, Indiana University, Bloomington, IN 47405, USA.

In addition to HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital), the Density program has been modified to handle keywords such as HOMO:n and LUMO:n (specifying HOMO - n and LUMO + n orbitals) and to write the output in Biosym's standard formatted grid file format.

The simplest description of how Ampac/Mopac works in the Insight II environment is that you select the various input options describing the molecular system, the type of the calculation, and the output desired, and then start the calculation. Insight II extracts the desired output from the Ampac/Mopac output files automatically and also generates the desired electron density and/or molecular orbital (M.O.) contours. The contours are generated from the <name>.grd files produced by running the Density program. The input file for the Density program, <name>.gpt, is generated by an AMPAC or MOPAC run.

By default, the AMPAC or MOPAC calculation optimizes the molecules and computes the partial atomic charges. The input file for the Density program, <name>.gpt, is also generated. If the appropriate options are selected, the Density program may be run once or multiple times to generate the grid file(s). When the background job completes, the output file containing the partial atomic charges and updated coordinates is read in automatically and a new molecule is created in Insight II. Based on the options specified for the calculation, electron density and/or M.O. contours are also generated automatically.

The following figure illustrates the data flow for the MOPAC program.

Geometry Optimization--The OPTIMIZE Suite of Algorithms


A powerful new suite of algorithms for geometry optimization, referred to collectively as OPTIMIZE, is now available for MOPAC calculations. Within the Insight environment, you can control OPTIMIZE via parameters provided in the Optimize/Parameters command; and specify its usage for a MOPAC calculation by choosing BIOSYM as Optimizer_Type for the AM_Setup/Calculation command.

OPTIMIZE is a general geometry-optimization package for locating both minima and transition states on a potential energy surface. It can optimize in Cartesian coordinates or in a non-redundant set of internal coordinates which are generated automatically from input Cartesian coordinates. It also handles fixed constraints on distances, bond angles, and dihedral angles in Cartesian or (where appropriate) internal coordinates - this feature is a key advantage of using OPTIMIZE in MOPAC calculations: it is not available in generic MOPAC, unless you go through a rather complicated process of editing the Z-matrix while maintaining its validity.

The MOPAC-BIOSYM-Optimizer methodology is iterative: repeated computation of energies and gradients (using MOPAC) and calculations (only the starting one; using MOPAC) or estimations (using OPTIMIZE) of hessians in every optimization cycle are performed until convergence is achieved. The Use_MOPAC_Hessian option, available through the AM_Setup/Calculation command, controls calculation of the starting hessian.

The following figure illustrates the MOPAC-BIOSYM-Optimizer methodology, along with the data flow among various programs.

Figure 27 . MOPAC-BIOSYM-Optimizer Cycle

a. mopacEnergy performs a MOPAC single point energy (1SCF) calculation to compute energy, gradient, and hessian (the last two items: first and second derivative of energy with respect to coordinate displacement respectively) - only the starting hessian is calculated; appropriate MOPAC keywords are retrieved from .keywords file. The .grad file contains energy and gradient information; hessian data are stored in the .hessian file.

b. OPTIMIZE is comprised of a suite of algorithms, developed at BIOSYM, to perform geometry optimizations. The .input file contains keywords to drive the geometry optimization. The .chk file checks information needed in the next cycle: updated hessian, etc. The final updated hessian is stored in the .hessian file; the user specified hessian file is renamed to .original.hessian.

c. Convergence criterion: energy differences, gradients, and displacements are less than tolerances specified in the .input file.

d. A single point energy (1SCF) calculation is performed on the optimized structure after extracting appropriate keywords from the .keywords file, to compute any user specified properties.

OPTIMIZE is designed to operate with minimal user input. All you need to provide is the initial geometry in Cartesian coordinates (obtained from the Insight or Discover programs or from an appropriate database), the type of stationary point sought (minimum or transition state), and details of any imposed constraints. All decisions as to the optimization strategy--how to handle the constraints, whether to use internal coordinates, which optimization algorithm to use--are made by OPTIMIZE.

You may, of course, override the default choices and force a particular optimization strategy, but there is no need to provide OPTIMIZE with anything other than the minimal information outlined above. In particular, you do not need to provide, for example, a Z-matrix or other connectivity data in order to take advantage of the potential efficiency gains associated with the use of internal coordinates. An excellent set of natural internal coordinates (Fogarasi et al. 1992, Baker 1993) can be generated automatically from the input Cartesians, and the optimization can be carried out using these coordinates.

The heart of the program (for both minimization and transition-state searches) is the EF (eigenvector-following) algorithm (Baker 1986). The Hessian mode-following option incorporated into this algorithm is capable of locating transition states by walking uphill from the associated minima. By following the lowest Hessian mode, the EF algorithm can locate transition states starting from any reasonable input geometry and Hessian.

An additional option available for minimization is GDIIS, which is based on the well known DIIS technique for accelerating SCF convergence (Pulay 1982).

The strategy adopted for constrained optimization depends on the starting geometry and the nature of the constraints. Constraints can be handled easily in internal coordinates, provided that (1) the constrained parameter (distance, angle, or dihedral) is a natural part of the coordinate set and (2) the constraint is rigorously satisfied in the starting structure. If both (1) and (2) hold for all desired constraints, then OPTIMIZE carries out the optimization in internal coordinates. Otherwise, the constrained optimization is performed in Cartesian coordinates.

Traditional wisdom has it that optimization in Cartesian coordinates is inefficient relative to internal coordinates; however, recent work (Baker and Hehre 1991) has clearly demonstrated that if a reasonable estimate of the Hessian matrix is available (e.g., from a molecular mechanics forcefield) at the starting geometry, optimization in Cartesian coordinates is as efficient as an internal coordinate optimization. In particular, constrained optimization can be handled in Cartesian coordinates as efficiently as with a Z-matrix, with the additional advantages that any distance, angle, or dihedral constraint between any atoms in the molecule can be dealt with (i.e., there is no formal connectivity requirement), and the desired constraint does not have to be satisfied in the starting structure.

OPTIMIZE incorporates a very accurate and efficient Lagrange multiplier algorithm for handling constraints in Cartesian coordinates, with a more robust (but less efficient) penalty function algorithm as a backup. Both algorithms are suitably modified versions of the basic EF algorithm (Baker 1992). The Lagrange multiplier algorithm can locate constrained transition states, as well as minima.

The original Lagrange multiplier algorithm has been significantly enhanced to incorporate both fixed and dummy atoms (Baker and Bergeron 1993). Standard distance and angle constraints can be specified with respect to dummy atoms, greatly extending the range of constraints that can be handled. Fixed atoms can be eliminated from the calculation (since there is no need to calculate their gradients), resulting in potentially significant savings of CPU time in ab initio computations.

Theory and Implementation

The EF Algorithm and Mode Following

Mode following is a powerful technique for geometry optimization. It involves modifying the standard Newton-Raphson step:

Eq. 7¯1            

by introducing a shift parameter so that (Cerjan and Miller 1981):

Eq. 7¯2            

In terms of a diagonal Hessian representation, this can be written:

Eq. 7¯3            

where the ui and bi are the eigenvectors and eigenvalues of the Hessian matrix H, and = uti g is the component of g along the local eigenmode ui. Scaling the Newton-Raphson step in this way has the effect of directing the step to lie primarily (but not exclusively) along one of the local eigenmodes, depending on the value chosen for .

Various recipes for choosing a suitable shift parameter exist: the EF algorithm utilizes a rational function approximation to the energy, yielding an eigenvalue equation of the form (Banerjee et al. 1985):

Eq. 7¯4            

from which a suitable can be obtained. This RFO matrix equation has the following important properties:

1.   The (n + 1) eigenvalues of Eq. 7-4 {i} bracket the n eigenvalues {bi} of the Hessian matrix, i bi i+1.

2.   At convergence to a stationary point, one of the eigenvalues of the RFO matrix is zero and the other n eigenvalues are those of the Hessian at the stationary point.

3.   For a saddlepoint of order m, the zero eigenvalue of the RFO matrix separates the m negative and the (n - m) positive Hessian eigenvalues.

Property 3--the separability of the positive and negative Hessian eigenvalues--allows two shift parameters p and n to be used, one for modes along which the energy is to be maximized and the other for which the energy is minimized. Specifically, for a transition state (a saddlepoint of order 1) in terms of the Hessian eigenmodes, we have the two matrix equations:

Eq. 7¯5            

Eq. 7¯6            

where it is assumed that maximization is along the lowest Hessian mode bi. Note that p is the highest eigenvalue of Eq. 7-5--it is always positive and approaches zero at convergence--while n is the lowest eigenvalue of Eq. 7-6--it is always negative and again approaches zero at convergence.

Choosing these values of gives a step that attempts to maximize along the lowest Hessian mode and minimize along all the others. It always does this regardless of the eigenvalue signature (unlike the standard Newton-Raphson step). The two shift parameters are then used in Eq. 7-4 to give a final step:

Eq. 7¯7            

This step may be further scaled down if it is considered too long. For minimization, only one shift parameter n would be used and this would act on all modes. It is often possible to locate different transition states from the same starting structure by maximizing along a mode other than the lowest (hence "mode following").

Constrained Optimization

The essential problem in constrained optimization is to minimize a function of, say, n variables F(x) subject to a series of m constraints of the form Ci(x) = 0, (i = 1 ... m). This can be handled by introducing the Lagrangian function (Fletcher 1981):

Eq. 7¯8            

which replaces the function F(x) in the unconstrained case. Here, the i are the so-called Lagrange multipliers, one for each constraint Ci(x). Taking the derivative of Eq. 7-8 with respect to x and gives:

Eq. 7¯9            


Eq. 7¯10            

At a stationary point of the Lagrangian function, we have L = 0, that is, all L \xda xj = 0 and all L \xda i = 0. This latter condition means that all Ci(x) = 0, and so all constraints are satisfied. Hence, finding a set of values (x, ) for which L = 0 gives a possible solution to the constrained optimization problem in precisely the same way as finding an x for which g = F = 0 gives a solution to the corresponding unconstrained problem.

We can implement mode following in constrained optimization by simply adopting Eq. 7-4, but with H replaced by 2L and g replaced by L. However, it is important to realize that each constraint introduces an additional mode to the Lagrangian Hessian (2L), which has negative curvature (a negative Hessian eigenvalue). Thus, when considering minimization with m constraints, you should look for a stationary point of the Lagrangian function whose Hessian has m negative eigenvalues, that is, for a saddle point of order m.

Insofar as mode following is concerned then, assuming a diagonal Lagrangian Hessian representation, Eqs. 7-5 and 7-6 for an unconstrained system should be replaced by the following for a constrained system:

Eq. 7¯11            

Eq. 7¯12            

where now the bi are the eigenvalues of 2L with corresponding eigenvectors ui and = uti L. Constrained transition-state searches can be carried out by selecting one extra mode to be maximized in addition to the m constraint modes, that is, by searching for a saddlepoint of the Lagrangian function of order m + 1.


In the GDIIS method, geometries (xi) generated in previous optimization steps are linearly combined to find the "best" geometry on the current cycle (Császár and Pulay 1984):

Eq. 7¯13            

The problem here, of course, is to find appropriate values for the coefficients Ci.

If we express each geometry (coordinate vector) by its deviation from the sought final converged geometry xf, that is, xi = xf + ei, then it is obvious that if the conditions:

Eq. 7¯14            


Eq. 7¯15            

are satisfied, then the relation:

Eq. 7¯16            

also holds.

The true error vectors ei are, of course, unknown. However, they can be approximated by:

Eq. 7¯17            

where gi is the gradient vector corresponding to the geometry xi. Minimization of the norm of the residuum vector (Eq. 7-14), together with the constraint equation (Eq. 7-15), leads to a system of m + 1 linear equations:

Eq. 7¯18            

where Bij = ei ej is the scalar product of the error vectors ei and ej, and is a Lagrange multiplier.

The coefficients Ci determined from Eq. 7-18 are used to calculate an intermediate interpolated geometry:

Eq. 7¯19            

and its corresponding interpolated gradient:

Eq. 7¯20            

A new, independent geometry is generated by relaxing the interpolated geometry according to:

Eq. 7¯21            

In the original GDIIS algorithm, the Hessian matrix is static, that is, the original starting Hessian remains unchanged during the entire optimization. However, updating the Hessian at each cycle generally results in more rapid convergence, and this is therefore the default in OPTIMIZE. Other modifications to the original method include limiting the number of previous geometries used in Eq. 7-13 by neglecting earlier geometries and eliminating any geometries more than a certain distance (default = 0.3 au) from the current geometry.

Command and Keyword Correspondance

The correspondence between various Insight II program parameters (first column) and AMPAC and MOPAC keywords (second column) is shown below.






Check_Input 0SCF

Energy 1SCF

Gradient NLLSQ

Frequency FORCE

Check_Frequency DUMP





Spin_Restricted RHF

Spin_Unrestricted UHF

Total_Charge CHARGE


Energy_Convergence SCFCRT

SCF_Iterations ITRY

Pulay_Convergence PULAY

Peptide_Correction MMOK, NOMM

Geometry_Test GEO-OK

Shift_Energy_Level SHIFT


Gradient_Converge GNORM






Max_Step_Size DMAX

Transition_State TS

Line_Minimizer NOTHIEL


Mulliken_Pop MULLIK

Overlap_Pop BONDS


Spin_Density SPIN

Polarizabilities POLAR

Thermodynamic_Prop THERMO(nnn,lll,mmm)

Electrostatic_Pot ESP


Constrain_Dipole DIPOLE

Scale_Radii SCALE

Scale_Charges SLOPE

Number_Surfaces NSURF

Output_Surface POTWRT

Enforce_Sym SYMAVG


Surface_Distance S CINCR




Minimize_Info FLEPO

Energy_Info ENPART

Frequency_Info FMAT

Interatomic_Dist NOINTER

Summary NOLOG


Gaussian_Z_Matrix AIGOUT


Safety_Checking LET

Based on the parameters selected for performing the Ampac/Mopac,
MOPAC-BIOSYM-Optimizer, and/or Density calculation in the Insight II environment, the following files could be produced.


<name>.dat Input

<name>.out Results

<name>.res Restart

<name>.den Density matrix (in binary)

<name>.log Log messages

<name>.arc Archive or summary

<name>.gpt Data for program Density (in binary)

<name>.syb SYBYL data

<name>.pot Surface points and electrostatic potential file from ESP option

ESP.DUMP Dump file from ESP option


<name>.dat Input

<name>.out Results

<name>.res Restart

<name>.den Density matrix (in binary)

<name>.log Log messages

<name>.arc Archive or summary

<name>.gpt Data for Density program (in binary)


<name>_density.gra Input referring to electron density

<name>_homo.gra Input referring to HOMO

<name>_lumo.gra Input referring to LUMO

<name>_homo_<n>.gra Input referring to (HOMO - n) orbital

<name>_lumo_<n>.gra Input referring to (LUMO + n) orbital

<name>_density.out Output referring to electron density

<name>_homo.out Output referring to HOMO

<name>_lumo.out Output referring to LUMO

<name>_homo_<n>.out Output referring to (HOMO - n) orbital

<name>_lumo_<n>.out Output referring to (LUMO + n) orbital

<name>_density.grd Grid produced by Density referring to electron density

<name>_homo.grd Grid produced by Density referring to HOMO

<name>_lumo.grd Grid produced by Density referring to LUMO

<name>_homo_<n>.grd Grid produced by Density referring to (HOMO - n) orbital

<name>_lumo_<n>.grd Grid produced by Density referring to (LUMO + n) orbital

MOPAC in conjunction with BIOSYM-Optimizer:

<name>.input Input for optimizer

<name>.keywords Keywords to control MOPAC calculation

<name>.opt.arc BIOSYM archive file

<name>.opt.log Log messages

<name>.hessian Hessian file updated by optimizer

<name>.original.hessian Original hessian file, if any

<name>.grad Gradient file

Background_Job Control:

Shell script for Ampac/Mopac job

Output from the shell script running the Ampac/Mopac job.

Two different contour objects are produced for each M.O. at plus and minus contour level values and are colored with appropriate colors. A grid object is also generated for each M.O. Based on the parameters selected, the following different objects can be generated automatically in the Insight II program.

Electron Density:

<name>_DEN_GRD Grid object

<name>_DEN_CNT Contour object


<name>_HO_GRD Grid object for HOMO

<name>_HOP_CNT Contour object for HOMO at plus contour level

<name>_HOM_CNT Contour object for HOMO at minus contour level

(HOMO - n):

<name>_HO<n>_GRD Grid object for (HOMO - n) orbital

<name>_HO<n>P_CNT Contour object for (HOMO - n) orbital at plus contour level

<name>_HO<n>M_CNT Contour object for (HOMO - n) orbital at minus contour level


<name>_LU_GRD Grid object for LUMO

<name>_LUP_CNT Contour object for LUMO at plus contour level

<name>_LUM_CNT Contour object for LUMO at minus contour level

(LUMO + n):

<name>_LU<n>_GRD Grid object for (LUMO + n) orbital

<name>_LU<n>P_CNT Contour object for (LUMO + n) orbital at plus contour level

<name>_LU<n>M_CNT Contour object for (LUMO + n) orbital at minus contour level

where n is an integer indicating the desired orbital lower than HOMO or higher than LUMO.

The following diagram illustrates the relationship between the molecule, grid objects, and contour objects.

The grid and contour objects can be manipulated by using the commands under the Grid and Contour pulldowns.

Command Summary

In addition to the core pulldowns in the top menu bar, the Ampac/Mopac module adds six pulldowns to the lower menu bar. The pulldowns are AM_Setup, AM_Parameters, Background_Job, AM_Run, AM_Analyze, and Grid.

AM_Setup Pulldown

The AM_Setup pulldown contains commands to set up the type of calculation to be performed on the system.


The System command is used to select the molecule to be studied. You can specify the program, AMPAC or MOPAC, to do the calculations.

The following options provided in the interface for MOPAC are not supported in AMPAC.








ELECTROSTATIC_POT and all the parameters related to it




Opt_Parameters and Constraints commands.


The Electronic_State command is used to specify the electronic state parameters of the system for Ampac/Mopac calculations. You can select a desired electronic state (singlet, doublet, etc.), choose a Hamiltonian (RHF, UHF), and specify the total charge on the system.


The Calculation command is used to set up the parameters for the type of calculation to be performed in Ampac/Mopac. Four different kinds of semiempirical methods (MNDO, MINDO/3, AM1, and PM3) are provided. Note that the PM3 method is not supported in AMPAC. You can specify an 0SCF or 1SCF calculation by selecting Check_Input or Energy from the various Calculation_Type options provided.


The List command is used to list the status of all the options available for all the commands in the AM_Setup pulldown.

Optimize Pulldown

The Optimization pulldown contains commands for setting up and controlling a geometry optimization. These commands are only available for a MOPAC calculation.


The Opt_Parameters command is used to specify parameters controlling the optimization calculation. Among other things, you can specify: locate minima or a transition state; use Cartesian or internal coordinates.


The Constraints command is used to set up geometric constraints imposed during the optimization calculation. You can specify fixed distance, angle, or dihedral constraints, or frozen atomic coordinates.

AM_Parameters Pulldown

The AM_Parameters pulldown contains commands to set up the parameters for the AMPAC or MOPAC calculation.


The SCF command is used to set up the parameters used in the SCF calculation in Ampac/Mopac.


The Optimize AM_Parameters command is used to specify the parameters controlling the optimization of the system during the Ampac/Mopac calculation.


The Saddle command in the AM_Parameters pulldown calls the Saddle AM_Parameters dialog box. This command allows you to specify the parameters that control the Saddle calculation.


The Properties command is used to specify the various parameters used in computing the desired properties of the system during the Ampac/Mopac calculation.


The Output command is used to control the various kinds of output produced from the Ampac/Mopac calculations. The automatic generation of contours for electron density and molecular orbitals (M.O.s) can also be specified. The different M.O.s available for generating contours are as follows:

· Highest Occupied Molecular Orbital (HOMO)

· Lowest Unoccupied Molecular Orbital (LUMO)

· Any orbital lower than HOMO

· Any orbital higher than LUMO


The Keywords command is used to specify additional keywords, by typing them explicitly, to fine tune the Ampac/Mopac calculation. It is useful for specifying the options not provided in the interface.


The List command is used to list the status of all the options available for all the commands in the Am_Parameters pulldown.

Background_Job Pulldown

The Background_Job pulldown allows you to set up background jobs to run concurrently or interactively with the Insight II program. You are given the choice of whether to send background jobs to a local or remote host. This pulldown is generic and is found in many modules that run background jobs. The Background_Job pulldown contains the following commands: Setup_Bkgd_Job, Control_Bkgd_Job, Completion_Status, and Kill_Bkgd_Job. Refer to the Background Job chapter for more information on background jobs and the commands in this pulldown.

AM_Run Pulldown

The AM_Run pulldown contains the command, AM_Run, to run an AMPAC or MOPAC job.


The Run command is used to run the Ampac/Mopac calculation. Based on the parameters selected in other commands, Density is also run (multiple times) automatically after the Ampac/Mopac calculation is finished.

AM_Analyze Pulldown

The AM_Analyze pulldown contains commands to generate the electron density and/or molecular orbital (M.O.) grid and contour objects from the <name>.gpt file (produced by a previous Ampac/Mopac run) by running the Density program without restarting AMPAC or MOPAC.

The commands under the AM_Analyze pulldown are useful for long Ampac/Mopac calculations. The Ampac/Mopac job can be started without selecting the options for automatic generation of the electron density and/or M.O. contours. After the job is finished, the desired electron density and/or M.O. contours can be easily generated using Electron_Density and Molecular_Orbital analysis commands from the file, <name>.gpt, produced by Ampac/Mopac. The <name>.gpt file is always produced by Ampac/Mopac running in the Insight II program environment.


The Electron_Density is used to generate the electron density grid and contour from the <name>.gpt file by running the Density program without restarting Ampac/Mopac. The grid and contour objects can be manipulated by using the commands under the Grid and Contour pulldowns.


The Molecular_Orbital is used to generate the molecular orbital (M.O.) grids and contours from <name>.gpt files, by running Density program successively without restarting AMPAC or MOPAC.

The grid and contour objects can be manipulated by using the commands under the Grid and Contour pulldowns.

Grid Pulldown (accessed from Grid icon)

The Grid pulldown is used to create and manipulate an energy grid for a given molecule. You may create and compute the energy grid, display and undisplay this grid, and write this grid to an output file (.grd) that is readable by commands in the Contour pulldown.

Get Grid

The Get Grid command retrieves grids from grid files.

Get Grid also assigns grid objects a default name based on the grid filename. If you specify an explicit grid name with the Grid Name parameter, the Get Grid command names the grid object according to the value you specified.

Put Grid

The Put Grid command allows you to write the grid data to a disk file. The output filename is specified in the File Name parameter. If no file extension is explicitly given, a default extension of .grd is tacked on.

The File Format option is used to specify the format of data written out. Binary, Ascii, and Voxel_View are the available choices.

Display Grid

The Display Grid command is used to control the display of grid objects. The Grid Attribute parameter controls which attribute of the grid to operate upon. The Point Set parameter specifies which points are set. The Alternate Space parameter specifies which axes to use.

Color Grid

The Color Grid command is used to control the color of grid objects. The Grid Attribute parameter controls which attribute of the grid to operate upon. The Point Set parameter specifies which points are set.

Histogram Grid

The Histogram Grid command plots a histogram of the scalar values of the grid points. The computation of histogram can be restricted to points with values which fall within a specified range. The coarseness of the histogram can be controlled by specifying the number of bins. The histogram can be plotted with linear or logarithmic scale heights.

The histogram is normally scaled so that the largest bin fills the full width of the display. An exception to this rule occurs when the number of grid data points is small. When the number of datapoints in the largest bin is less than the maximum bar length, the histogram is unscaled. In other words, the number of asterisks ("*") plotted is equal to the actual integer number of points in each bin or (when the logarithm option is selected) the logarithm of the integer values.

The number of points lying outside the lower and upper limits (i.e., the "tails" of the distribution) are demarked by the symbols [<<<] and [>>>], respectively.

Contour Grid

The Contour Grid command creates a set of contours from a specified Insight II grid. One contour is created for each of the contour levels you specify. A contour level is a curve (or surface) connecting all of the points in the given grid which have the same value. These contours may be automatically displayed or left undisplayed. Note that the contours may be written to a file with the Save_Folder command.

The Molecule Name parameter is the name of the molecule for which the grid to be contoured is defined.

The Contour Name Root parameter specifies the prefix of the names of the resulting contour objects. Each name consists of this string with a digit appended on the end to make the name unique. For example, if Contour Name Root is CNT and there are three levels specified, the resulting contour names are CNT0, CNT1, and CNT2. The next contour created using CNT is named CNT3.

You may create a Single or a Range of individual contour levels with the Level Specification option, adding a range of levels with the Interval_Size and Number_Of_Levels options.

Slice Grid

The Slice Grid command allows you to analyze grid data by positioning a two dimensional slicing plane inside the grid and visualize the grid values interpolated to that plane either as a continuously colored mapplane, or as a number of 2d isovalue contours. The mapping from data value to color is controlled via a spectrum.

Label Grid

The Label Grid command is used to create and position or to remove labels for grid objects. The object name of the grid becomes the contents of the label. The Label Coordinates parameter controls the positioning of the label.

CharSize Grid

The CharSize Grid command modifies the size of the specified grid object's label.

List Grid

The List Grid command displays information pertaining to the structure and display of a grid object. The Detail Level parameter controls the amount of information provided. Use the Output_File parameter to direct the display of the command output to either the textport or a file.

Ampac/Mopac Tutorial

As of this release, most tutorials are now available online for use with the Pilot interface. To access the online tutorials for Ampac/Mopac, click the mortarboard icon in the Insight II interface.

Then, from the Open Tutorial window, select Insight II tutorials, then the Ampac/Mopac module and choose from the list of available lessons:

Lesson 1 Basic Functionality of MOPAC

Lesson 2 Use Post-Processing to Generate Contours

Lesson 3 Finding the Transition State of a Reaction

Lesson 4 Slicing Plane tutorial

Lesson 5 Characterization of the Potential Energy surface of 1,3-butadiene

Lesson 6 Finding a transition state

You can access the Open Tutorial window at any time by clicking the Open File button in the lower left corner of the Pilot window.

For a more complete description of Pilot and its use, click the on-screen help button in the Pilot interface or refer to the Insight II User Guide.

Last updated December 17, 1998 at 04:27PM PST.
Copyright © 1998, Molecular Simulations Inc. All rights reserved.