Dresden 2020 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 2: Topical Session: Data Driven Materials Science - Materials Design I (joint session MM/CPP)
MM 2.4: Talk
Monday, March 16, 2020, 11:15–11:30, BAR 205
Towards Building New Zeolites with Machine Learning — •Benjamin A. Helfrecht1, Rocio Semino1, 2, Giovanni Pireddu1, 3, Scott M. Auerbach4, and Michele Ceriotti1 — 1École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland — 2Université de Montpellier, Montpellier, France — 3Università degli Studi di Sassari, Sassari, Italy — 4University of Massachusetts Amherst, Amherst, Massachusetts USA
Synthesizing new zeolites, which are useful for applications like gas separation and catalysis, with specific properties is an ongoing challenge in the zeolite community. Ideally, one would like to select a handful of compatible “building blocks” from which a new zeolite with desired properties can be synthesized. In this work, we make progress toward this goal by constructing an “atlas” of local atomic environments comprising several thousand all-silica zeolites from the Deem SLC PCOD database [1] using machine learning techniques. We evaluate the utility of this atlas by examining correlations between the locations of the atomic environments in the atlas and their energy and volume contributions to their parent frameworks.
[1] R. Pophale, P. A. Cheeseman, M. W. Deem, A database of new zeolite-like materials, Phys. Chem. Chem. Phys 13(27):12407-12412, 2011.