The Otago Research Genetic Algorithm for Nanoclusters, Including Structural Methods and Similarity (Organisms) Program: A Genetic Algorithm for Nanoclusters
Authors: Dr. Geoffrey R. Weal and Dr. Anna L. Garden (University of Otago, Dunedin, New Zealand)
Group page: https://blogs.otago.ac.nz/annagarden/
Page to cite with work from: Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimisation of Nanoclusters, Geoffrey R. Weal, Samantha M. McIntyre, and Anna L. Garden, J. Chem. Inf. Model., 2021, 61 (4), 1732–1744, DOI: 10.1021/acs.jcim.0c01128
The Otago Research Genetic Algorithm for Nanoclusters, Including Structural Methods and Similarity (Organisms) program is designed to perform a genetic algorithm global optimisation for nanoclusters. It has been designed with inspiration from the Birmingham Cluster Genetic Algorithm and the Birmingham Parallel Genetic Algorithm from the Roy Johnston Group (see J. B. A. Davis, A. Shayeghi, S. L. Horswell, R. L. Johnston, Nanoscale, 2015,7, 14032 (https://doi.org/10.1039/C5NR03774C or link to pdf here), R. L. Johnston,Dalton Trans., 2003, 4193–4207 (https://doi.org/10.1039/B305686D or link to pdf here
If you are new to the Organisms program, it is recommended try it out by running Organisms live on our interactive Jupyter+Google Colabs page before you download it. On Google Colabs, you can play around with the Organisms program on the web. You do not need to install anything to try Organisms out on Google Colabs.
Click the Google Colabs button below to try Organisms out on the web!
Have fun!
This program has been designed to learn about how to improve the efficiency of the genetic algorithm in locating the global minimum. This genetic algorithm implements various predation operators, fitness operators, and epoch methods. A structural comparison method based on the common neighbour analysis (CNA) has been implemented into a SCM-based predation operator and ''structure + energy'' fitness operator.
The SCM-based predation operator compares the structures of clusters together and excludes clusters from the population that are too similar to each other. This can be tuned to exclude clusters that are structurally very similar to each other, to exclude clusters that are structurally different but of the same motif, or set to a custom structural exclusion setting.
The ''structure + energy'' fitness operator is designed to include a portion of structural diversity into the fitness value as well as energy. The goal of this fitness operator is to guide the genetic algorithm around to unexplored areas of a cluster's potential energy surface.
This genetic algorithm has been designed with Atomic Simulation Environment (ASE, https://wiki.fysik.dtu.dk/ase/). with the use of ASE, clusters that are generated using the genetic algorithm are placed into databases that you can assess through the terminal or via a website. See more about how to the ASE database works in the link here.
The CNA has been implemented using ASAP3 (As Soon As Possible). See https://wiki.fysik.dtu.dk/asap for more information about ASAP3.
It is recommended that you install a release version of the Organisms program, as these release versions are tested to make sure they (hopefully) run properly before they are released. You can find out how to install a release version of Organisms program on the installation page of the Organisms documentation in the link below:
organisms.readthedocs.io/en/latest/Installation.html
This includes instructions on how to clone the latest releast version of Organisms from Github. Note that you can install Organisms through pip3 and conda.
All the information about this program is found online at organisms.readthedocs.io/en/latest/. Click the button below to also see the documentation:
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| Authors | Dr. Geoffrey R. Weal, Dr. Anna L. Garden |
| Group Website | https://blogs.otago.ac.nz/annagarden/ |