New release: matlantis-contrib repository
We have published the matlantis-contrib repository which contains useful examples and simulation scripts for Matlantis™, our atomistic simulator that accelerates research and development in computational chemistry.
On Matlantis, you can run simulations in a Jupyter Lab environment using the Python programming language, in which you can interactively check your calculation, build new hypotheses and proceed with experiments, and flexibly perform a variety of calculations. It can take a while to familiarize yourself with the environment, however, due to its difference from the conventional DFT (density functional theory) and MD (molecular dynamics) tools.
To facilitate your initial learning and also to promote material discovery using NNP (Neural Network Potential), we will be adding a range of calculation examples to the repository.
Currently, the repository includes the following calculation examples:
- LGPS – Li Diffusion (battery)
- BaTiO3 – Phase transition analysis with RDF (dielectric material)
- NO dissociation on Rh (reaction analysis on catalysts)
Methods for generating crystal and liquid structures as well as useful scripts for visualizing atomic systems are also available in the repository.
We also welcome contributions from all Matlantis users to the repository. We are constantly looking for simulation codes for various material domains, structures, and phenomena. If you have any good results, feel free to send pull requests to this repository!
We hope to see more advancement in computational chemistry for materials discovery as more calculation case studies are made available to the public and that will help many researchers discover innovative materials for our sustainable future. We look forward to your contribution!