SKM 2023 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
MM: Fachverband Metall- und Materialphysik
MM 31: Data Driven Materials Science: Big Data and Work Flows – Machine Learning
MM 31.6: Vortrag
Mittwoch, 29. März 2023, 17:15–17:30, SCH A 251
Accelerating the Search for High-Performance, Novel Materials with Active Learning — Thomas A. R. Purcell1, Matthias Scheffler1,2, Luca M. Ghiringhelli1,2, and •Christian Carbogno1 — 1The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin — 2Physics Department and IRIS-Adlershof at Humboldt Universität zu Berlin, Berlin, Germany.
Active-learning frameworks have the potential to greatly accelerate the search for new materials. By balancing exploitation and exploration, these approaches can efficiently search through materials space and find the regions that are most likely to contain promising candidate materials [1]. Here we present an active learning framework, that uses an ensemble of expressions found by the sure-independence screening and sparsifying operator (SISSO) approach [2,3], and we demonstrate it for the example of discovering new thermal insulators. We statistically process the predictions of independent SISSO models to automatically select the most promising material candidates and then calculate their thermal conductivity κL using the ab initio Green Kubo method [4]. Using this approach we are able to find multiple new thermal insulators and gain insights into what is driving down their κL.
[1] A. G. Kusne, et al. Nat. Comm. 11, 5966 (2020)
[2] R. Ouyang, et al. Phys. Rev. Mater. 2, 083802 (2018)
[3] T. A. R. Purcell, et al. J. Open Source. Softw. 7, 3960 (2022)
[4] F. Knoop, et al. arXiv:2209.12720