Berlin 2018 – scientific programme
Parts | Days | Selection | Search | Updates | Downloads | Help
MM: Fachverband Metall- und Materialphysik
MM 61: Topical Session (Symposium MM): Big Data in Materials Science - Managing and exploiting the raw material of the 21st century
MM 61.4: Talk
Thursday, March 15, 2018, 16:45–17:00, H 0107
SISSO: a Compressed-Sensing Method for Systematically Identifying Efficient Physical Models of Materials Properties — •Runhai Ouyang1, Stefano Curtarolo2, Emre Ahmetcik1, Matthias Scheffler1, and Luca M. Ghiringhelli1 — 1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin-Dahlem, Germany — 2Materials Science, Duke University, Durham, NC, USA
We present a systematic data-driven approach for discovering physically interpretable descriptors and predictive models, within the framework of compressed sensing. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials’ property of interest. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal-insulator classification (with experimental data). Accurate predictive models are found in both cases. For the metal-insulator classification model, the interpretability and predictive capability are tested beyond the training data: It perfectly rediscovers the available pressure-induced insulator to metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation.