pySIPFENN
py (S tructure - I nformed P rediction of F ormation E nergy using N eural N etworks) software package allows efficient predictions of the energetics of atomic configurations. The underlying methodology and implementation is given in
Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu, Zi-Kui Liu, Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks, Computational Materials Science, Volume 208, 2022, 111254 (https://doi.org/10.1016/j.commatsci.2022.111254)
News
(v.12.2) The license has been changed to LGPLv3 to allow for integration with proprietary software developed by CALPHAD community, while supporting the development of new pySIPFENN features for all users. Many thanks to our colleagues from GTT-Technologies and other participants of 50th CALPHAD 2023 conference in Boston for fruitful discussions.
(v.12.0) Official Python 3.11 support.
(v.12.0) Automated matrix-testing on Linux / Mac / Windows with Python 3.9 / 3.10 / 3.11 through GitHub Actions CLI and test coverage report through Codecov. Tests are also generally improved and more extensive.
(v0.11.0) Some common questions are now addressed in the documentation FAQ section.
(v0.11.0) The model downloads from Zenodo are now multithreaded and are 15 times faster.
(March 2023 Workshop) We would like to thank all of our amazing attendees for making our workshop, co-organized with the Materials Genome Foundation, such a success! Over 100 of you simultaneously followed all exercises and, at the peak, we loaded over 1,200GB of models into the HPC’s RAM. At this point, we would also like to acknowledge the generous support from IBM who funded the workshop. Please stay tuned for next workshops planned online and in-person at conferences. They will be announced both here and at the Materials Genome Foundation website.
Note
This project is under active development. We recommend using released (stable) versions.
Index
Applications
pySIPFENN is a very flexible tool that can, in principle, be used for the prediction of any property of interest that depends on an atomic configuration with very few modifications. The models shipped by default are trained to predict formation energy because that is what our research group is interested in; however, if one wanted to predict Poisson’s ratio and trained a model based on the same features, adding it would take minutes. Simply add the model in open ONNX format and link it using the models.json file, as described in the documentation.
Real-World Examples
In our line of work, pySIPFENN and the formation energies it predicts are usually used as a computational engine that generates proto-data for creation of thermodynamic databases (TDBs) using ESPEI (https://espei.org). The TDBs are then used through pycalphad (https://pycalphad.org) to predict phase diagrams and other thermodynamic properties.
Another of its uses in our research is guiding the Density of Functional Theory (DFT) calculations as a low-cost screening tool. Their efficient conjunction then drives experiments leading to the discovery of new materials, as presented in these two papers:
Sanghyeok Im, Shun-Li Shang, Nathan D. Smith, Adam M. Krajewski, Timothy Lichtenstein, Hui Sun, Brandon J. Bocklund, Zi-Kui Liu, Hojong Kim, Thermodynamic properties of the Nd-Bi system via emf measurements, DFT calculations, machine learning, and CALPHAD modeling, Acta Materialia, Volume 223, 2022, 117448, https://doi.org/10.1016/j.actamat.2021.117448.
Shun-Li Shang, Hui Sun, Bo Pan, Yi Wang, Adam M. Krajewski, Mihaela Banu, Jingjing Li & Zi-Kui Liu, Forming mechanism of equilibrium and non-equilibrium metallurgical phases in dissimilar aluminum/steel (Al–Fe) joints. Nature Scientific Reports 11, 24251 (2021). https://doi.org/10.1038/s41598-021-03578-0