About

Contains the reproduction details for the publication on the performance and success models for the dimer across rotational optimizers and external rotation removal.

Reference

If you use this repository or its parts please cite the corresponding publication or data source.

[1] R. Goswami, “Bayesian hierarchical models for quantitative estimates for performance metrics applied to saddle search algorithms,” AIP Adv., vol. 15, no. 8, p. 85210, Aug. 2025, doi: 10.1063/5.0283639.

Preprint

Also on ArXiv:

R. Goswami, “Bayesian hierarchical models for quantitative estimates for performance metrics applied to saddle search algorithms,” May 19, 2025, arXiv: arXiv:2505.13621. doi: 10.48550/arXiv.2505.13621.

Data source

Rohit Goswami, Bayesian hierarchical models for quantitative estimates for performance metrics applied to saddle search algorithms, Materials Cloud Archive 2025.X (2025),

Replication data

Remember to inflate the data using the materialscloud source before using the scripts in the repository. This can be done by running the following–assuming that the .xz files are in data relative to the repository root:

# Fitted models with predictions
cd $GITROOT/data
tar -xf models_and_preds.tar.xz && rm -rf models_and_preds.tar.xz
# Raw benchmark data, i.e., EON output logs
cp $GITROOT/data/hpc.tar.xz $GITROOT/bench_runs/runs/hpc
cd $GITROOT/bench_runs/runs/hpc
tar -xf hpc.tar.xz && rm -rf hpc.tar.xz

Structure

The repository itself is structured into code archives, benchmark runs, and scripts for analysis.

➜ tree -L 2
.
├── bench_runs
│   ├── base_config.ini
│   ├── calc_rundata.py
│   ├── profiles
│   ├── readme.org
│   ├── rundata
│   ├── run_eon.py
│   ├── scripts
│   └── Snakefile
├── data
│   └── sella_si_data.zip
├── docs
│   └── source
├── LICENSE
├── pixi.lock
├── pixi.toml
├── readme.org
├── scripts
│   └── env_setup.sh
└── subrepos
    ├── ase
    ├── chemparseplot
    ├── eOn
    ├── IterativeRotationsAssignments
    ├── nwchem
    ├── pychumpchem
    └── rgpycrumbs

Where the data in the archives expands to locations within the benchmarks.

Each of the benchmarks consists of the following structure:

.
├── doublets
│   ├── 000
# .....
│   └── 234
└── singlets
│   ├── 000
# .....
    └── 264

Comprising of 500 systems.

EON Dimer runs

# hpc.tar.xz
# $GITROOT/bench_runs/runs/hpc
➜ tree -L 3 .
.
├── cg
│   ├── no_rot_remove
│   │   ├── doublets
│   │   └── singlets
│   └── rot_remove
│       ├── doublets
│       └── singlets
└── lbfgs
    ├── no_rot_remove
    │   ├── doublets
    │   └── singlets
    └── rot_remove
        ├── doublets
        └── singlets

Usage

A reproducible setup for generating benchmarks from the sella [6] test systems for comparing against the Dimer methods [1,2,3,5] implemented in eOn [4].

Git subrepo can be a pain, NEVER force push or rebase the branchs linked in the repo. Regular subrepo based push and pull work just fine.

Setup

Everything is part of the repository, but eOn needs to be installed.

cd subrepos/eOn
meson setup --reconfigure bbdir --prefix=$CONDA_PREFIX -Dwith_ase_nwchem=True -Dwith_python=True --buildtype release --libdir=lib
meson install -C bbdir

Now everything is ready for reproduction. Several pixi groups are defined to ensure minimal conflits. None of these contain eOn so the instructions above need to be followed for each environment where it is necessary.

Remember to install NWChem from the subrepos folder as well.

Analysis

pixi s -e analysis

This contains R and Python plotting dependencies.

For generating the IRA measures remember to set things up:

cd subrepos IterativeRotationsAssignments/src
make all

References

1. Henkelman, G. & Jónsson, H. A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives. The Journal of Chemical Physics 111, 7010–7022 (1999).
2. Olsen, R. A., Kroes, G. J., Henkelman, G., Arnaldsson, A. & Jónsson, H. Comparison of methods for finding saddle points without knowledge of the final states. J. Chem. Phys. 121, 9776–9792 (2004).
3. Kästner, J. & Sherwood, P. Superlinearly converging dimer method for transition state search. J. Chem. Phys. 128, 014106 (2008).
4. Chill, S. T. et al. EON: software for long time simulations of atomic scale systems. Modelling Simul. Mater. Sci. Eng. 22, 055002 (2014).
5. Melander, M., Laasonen, K. & Jónsson, H. Removing External Degrees of Freedom from Transition-State Search Methods using Quaternions. J. Chem. Theory Comput. 11, 1055–1062 (2015).
6. Hermes, E. D., Sargsyan, K., Najm, H. N. & Zádor, J. Sella, an Open-Source Automation-Friendly Molecular Saddle Point Optimizer. J. Chem. Theory Comput. 18, 6974–6988 (2022).

License

MIT. Sub-packages have their own licenses.