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

University of Alberta

Edmonton

Canada

ACA

 

Invited speakers

Name

Family

Institution

City

Country

Title

Wenhao

Sun

Department of materials Science and Engineering University of Michigan

Ann Arbor

USA

Unsupervised Knowledge Discovery in ‘Big’ Materials Data

Aria Mansouri

Tehrani

Department of Materials, ETH

Zürich

Switzerland