OPTIM is distributed free of charge under the GPL as
part of the current svn software image from the group.

The Makefiles assume that the source is located in /home/$USER/svn.

- Example PUSHOPT runs for a machine learning example with BFGSTS and SEARCH 0 transition
state seacrhes combined with pathways calculated using LBFGS, SEARCH 0, and Page-McIver steepest-descent.

- Example MULTIJOB run for tighter minimisation of LJ
_{31}minima in either xyz or coordinate list format.

- Example MULTIJOB run for double-ended connections of LJ
_{31}minima in either xyz or coordinate list format using NEWCONNECT.

- Example connection for bulk LJ
_{864}with periodic boundary conditions.

- Example calculations for the LJ
_{38}cluster.

Double-ended transition state searches and pathways using NEWCONNECT. In each case a path.info file is produced using the DUMPALLPATHS keyword for use by the PATHSAMPLE program.

Eigenvector-following transition state searches with LBFGS energy minimisation for the pathways

Hybrid eigenvector-following transition state searches with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and LBFGS energy minimisation for the pathways

Hybrid eigenvector-following transition state searches with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine and LBFGS energy minimisation for the pathways

Hybrid eigenvector-following transition state searches using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction and LBFGS energy minimisation for the pathways

Single-ended transition state searches and pathways. In each case a path.info file is produced using the DUMPALLPATHS keyword for use by the PATHSAMPLE program, in addition to the energy as a function of integrated path length (EofS) and a movie of the complete path (points.path.xyz)

Eigenvector-following transition state search with Runge-Kutta energy minimisation for the pathway

Eigenvector-following transition state search with Page-McIver energy minimisation for the pathway

Eigenvector-following transition state search with LBFGS energy minimisation for the pathway

Eigenvector-following transition state search with eigenvector-following energy minimisation for the pathway

Eigenvector-following transition state search with Bulirsch-Stoer energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine then Bulirsch-Stoer energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine then Runge-Kutta energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine then Page-McIver energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine then LBFGS energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction using the DSYEVR routine then eigenvector-following energy minimisation for the pathway

Hybrid eigenvector-following transition state search using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction then Bulirsch-Stoer energy minimisation for the pathway

Hybrid eigenvector-following transition state search using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction then Runge-Kutta energy minimisation for the pathway

Hybrid eigenvector-following transition state search using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction then Page-McIver energy minimisation for the pathway

Hybrid eigenvector-following transition state search using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction then LBFGS energy minimisation for the pathway

Hybrid eigenvector-following transition state search using a gradient-only variational approach for the eigenvalue and eigenvector corresponding to the uphill direction then eigenvector-following energy minimisation for the pathway

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and LBFGS energy minimisation for the pathways

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and Bulirsch-Stoer energy minimisation for the pathways

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and Runge-Kutta energy minimisation for the pathways

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and Page-McIver energy minimisation for the pathways

Hybrid eigenvector-following transition state search with calculation of the eigenvalue and eigenvector corresponding to the uphill direction performed using an iterative approach for the full Hessian and eigenvector-following energy minimisation for the pathways

- Factorised superposition approach (FSA) for computing binding free energies (AMBER9)
- REDOPATH movie-making run for a trapped ion cluster
- NEWCONNECT run for TIP4P water octamer
- NEWCONNECT run for TIP4P water octamer using the angle-axis scheme
- Harmonic normal mode analysis on a folded conformation of the met-enkephalin peptide (AMBER9)
- Harmonic normal mode analysis on a folded conformation of the met-enkephalin peptide (CHARMM19)
- Transition state benchmark for a amyloidogenic GNNQQNY peptide rearrangement (CHARMM19)
- Transition state benchmark for a tryptophan zipper 1 rearrangement (AMBER9)
- The MSB test sets for pathways in trpzip1 and GNNQQNY with CHARMM and AMBER. These compressed tar files contain a total
of 2100 pathways, including OPTIM input and output. Each double-ended connection attempt is performed with
quasi-continuous interpolation (QCI), DNEB interpolation, and the seven interpolation schemes described in
M.S. Bauer, B. Strodel, S.N. Fejer, E.F. Koslover and D.J. Wales, J. Chem. Phys., 132, 054101 (2010).
AMBER/GNNQQNY/TS1

AMBER/GNNQQNY/TS2

AMBER/GNNQQNY/TS3

AMBER/trpzip/TS1

AMBER/trpzip/TS2

AMBER/trpzip/TS3

CHARMM/GNNQQNY/TS1

CHARMM/GNNQQNY/TS2

CHARMM/GNNQQNY/TS3

CHARMM/trpzip/TS1

CHARMM/trpzip/TS2

CHARMM/trpzip/TS3