Implementation of the SISSO regression algorithm in MATLAB. The SISSO regression algorithm iteratively selects model features from candidates, converging even when the number of possible features is much greater than the number of available data points.

Includes a test script that validates the algorithm by replicating the results using data and procedure from https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/compressed_sensing.ipynb.

Python code from the SISSO regressor in 'sisso.py', from the link above, was used as the basis for developing the MATLAB implementation. The SISSO regression algorithm is detailed by the original authors in R. Ouyang, S. Curtarolo, E. Ahmetcik et al., Phys. Rev. Mater. 2, 083802 (2018), R. Ouyang, E. Ahmetcik, C. Carbogno, M. Scheffler, and L. M. Ghiringhelli, J. Phys.: Mater. 2, 024002 (2019).

Included is also a function, generateDescriptors, which is an example of how to generate a library of equation descriptors from some input features using a defined set of operators. This function serves as an example, and should be tailored to suit the needs of each application using domain knowledge.

A more efficient Fortran implementation can be found at https://github.com/rouyang2017/SISSO.

Created and maintained by Paul Gasper (Paul.Gasper@nrel.gov, pauljgasper@gmail.com), feel free to contact for questions about the implementation or use of SISSO in MATLAB.