MS details

The schedule is available at https://www.conftool.com/iucr2020/

Data-driven discovery in crystallography

Comments

The mining of large datasets and databases is now commonplace pursuit in science, and data-driven discovery has become an essential component of various fields of research (e.g., recommendation engines in materials sciences, data-driven optimization in engineering) and a significant contributing factor to their prolific output. We propose to organize a session that will focus on the promotion and integration of data-driven discovery in crystallography, with primary focus on minerals, inorganic materials, and extended inorganic solids. This session will showcase recent works that have employed large data resources, computational-driven approach, machine-learning guidance, and advanced analytical methods to realize large-scale patterns in the solid state leading to discovery.

Chair persons

Name	Family	Institution	City	Country	Region
Olivier	Gagné	Carnegie Institution for Science	Washington DC	USA	ACA
Anton	Oliynyk	Manhattan College	New York	USA	ACA

Invited speakers

Name	Family	Institution	City	Country	Title
Wenhao	Sun	University of Michigan	Ann Arbor	USA	Unsupervised Knowledge Discovery in ‘Big’ Materials Data
Aria Mansouri	Tehrani	Department of Materials, ETH	Zürich	Switzerland	Predicting ground state and metastable crystal structures using elemental and phonon mode