diff --git a/docs/index.rst b/docs/index.rst index 36d23b0..87dbc81 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -46,7 +46,8 @@ 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:** +News +---- - **(v0.11.0)** Some common questions are now addressed in the `documentation FAQ @@ -54,7 +55,7 @@ is given in - **(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-orgazined with the `Materials + 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 @@ -67,6 +68,9 @@ is given in .. note:: This project is under active development. We recommend using released (stable) versions. +Index +----- + .. toctree:: install faq @@ -77,3 +81,43 @@ is given in genindex :maxdepth: 2 :caption: Contents + +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