Data-driven Discovery of New Two- and One-dimensional Materials and Lattice-commensurate Heterostructures
ORAL
Abstract
We employ data-driven methods to discover new two- and one-dimensional materials. Layered materials have attracted interest for technological applications and fundamental physics. But only a few van der Waals solids have been subject to considerable research focus. Through data mining, we identify 1173 two-dimensional layered materials and 487 weakly bonded one-dimensional molecular chains. This is an order of magnitude increase in the number of known materials. Moreover, we discover 98 heterostructures of two-dimensional and one-dimensional subcomponents that are found within bulk materials, opening new possibilities for van der Waals heterostructures.
To identify these materials, we present a novel data mining algorithm that determines the dimensionality of weakly bonded subcomponents. Chemical families, band gaps, and point groups and single-layer piezoelectricity of the materials identified with data mining are presented. Moreover, we expand on this work to new material compositions that can form layered materials.
To identify these materials, we present a novel data mining algorithm that determines the dimensionality of weakly bonded subcomponents. Chemical families, band gaps, and point groups and single-layer piezoelectricity of the materials identified with data mining are presented. Moreover, we expand on this work to new material compositions that can form layered materials.
*Supported by Army Research Office W911NF-15-1-0570, Office of Naval Research N00014-15-1-2697, U.S. Army Research Laboratory W911NF-07-0027, NSF Grant DMR-1455050 & EECS-1436626, Stanford Graduate Fellowship
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Presenters
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Gowoon Cheon
- Stanford University
- Stanford Univ