Dresden 2020 – scientific programme
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SYBD: Symposium Big data driven materials science
SYBD 1: Big Data Driven Materials Science
SYBD 1.2: Invited Talk
Tuesday, March 17, 2020, 10:00–10:30, HSZ 02
Network Theory Meets Materials Science — •Chris Wolverton1, Murat Aykol2, and Vinay Hegde3 — 1Northwestern University, Evanston, IL, USA — 2Toyota Research Institute, Los Altos, CA, USA — 3Citrine Informatics, Redwood City, CA, USA
One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We demonstrate the utility of applying network theory to materials science in two applications: First, we unravel the complete *phase stability network of all inorganic materials* as a densely-connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie-lines (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. Using the connectivity of nodes in this phase stability network, we derive a rational, data-driven metric for material reactivity, the nobility index, and quantitatively identify the noblest materials in nature. Second, we apply network theory to the problem of synthesizability of inorganic materials, a grand challenge for accelerating their discovery using computations. We use machine-learning of our network to predict the likelihood that hypothetical, computer generated materials will be amenable to successful experimental synthesis.