| SEARCH | 2 |
| MODE | 2 |
| MAXSTEP | 0.1 |
| TRAD | 2.0 |
| STEPS | 100 |
| CONVERGE | 0.005 0.000001 |
| POINTS | |
| etc. |
The next dataset is the same as above except that we perform an eigenvector-following minimization and set an initial displacement from the assumed transition state of 0.05 units.
| SEARCH | |
| PUSHOFF | 0.05 |
| MAXSTEP | 0.1 |
| TRAD | 2.0 |
| STEPS | 100 |
| CONVERGE | 0.005 0.000001 |
| POINTS | |
| etc. |
Next is a conjugate gradient optimization with a maximum of 100 steps and convergence conditions of 0.00001 and 0.000001 on the change in energy and RMS gradient, respectively. There follows a check of the Hessian index using iteration to calculate eigenvalues to an accuracy of 0.01 with 200 and 50 iterations maximum for the smallest and largest eigenvalues, respectively.
| STEPS | 100 |
| CGMIN | 0.00001 0.000001 |
| CHECKINDEX | 200 0.01 50 |
| POINTS | |
| etc. |
Next is a transition state search using the hybrid EF/CG method. The maximum EF step and trust radius are 0.1 and 2.0, respectively. The maximum number of combined EF/CG steps is 100. The maximum number of iterations allowed is 200 for the smallest and 50 for the largest eigenvalue. A maximum of 20 conjugate gradient steps is permitted in the orthogonal subspace after each eigenvector-following step, and the convergence criterion on the Hessian eigenvalues is 0.01. The convergence criteria for each conjugate gradient subspace minimization are 0.001 and 0.0001 on the energy change and RMS gradient, respectively. Convergence is achieved when the maximum unscaled eigenvector-following step falls below 0.01, the total RMS force falls below 0.001, and the previous subspace minimization has converged. The smallest eigenvalue and the corresponding eigenvector are saved in file vector.dump. The Hessian index is checked using iteration to calculate the eigenvalues with the same parameters as the CGTS keyword.
| MAXSTEP | 0.1 |
| TRAD | 2.0 |
| STEPS | 100 |
| CGTS | 200 20 0.01 50 |
| CGCONV | 0.001 0.0001 |
| CONVERGE | 0.01 0.001 |
| DUMPVECTOR | |
| CHECKINDEX | |
| POINTS | |
| etc. |
The following odata file is the same as above, except that no Hessian is available. A maximum of 200 steps are permitted in the conjugate gradient minimization which is designed to produce the lowest eigenvalue, and the convergence condition on this eigenvalue is a change of 0.01 between CG steps. Otherwise the parameters have the same meanings as above.
| MAXSTEP | 0.1 |
| TRAD | 2.0 |
| STEPS | 100 |
| NOHESS | |
| CGTS | 200 20 0.01 |
| CGCONV | 0.001 0.0001 |
| CONVERGE | 0.01 0.001 |
| DUMPVECTOR | |
| CHECKINDEX | |
| POINTS | |
| etc. |
In the final example we calculate the part of the reaction pathway resulting from a displacement of 0.05 antiparallel to the transition vector saved in file vector.dump. No parameters are needed for CGTS because the transition vector is being supplied. Although the CGTS keyword is present, the calculation reverts to a pure conjugate gradient minimization after the first step.
| MAXSTEP | 0.1 |
| TRAD | 2.0 |
| STEPS | 100 |
| CGSTEP | |
| PUSHOFF | 0.05 |
| MODE | -1 |
| READVEC | |
| CGTS | |
| CGONV | 0.001 0.0001 |
| CONVERGE | 0.01 0.001 |
| POINTS | |
| etc. |