Description:
This session invites submissions on experimental and computational research that aims to understand and design materials and related processes using data-driven methods. Data-driven approaches include, but are not limited to, high-throughput screening, machine learning, data mining, meta-analysis, accelerated simulation techniques, and model prediction based on data. We strongly encourage abstracts that integrate data science with classical or quantum simulation and experimental approaches for materials design and property prediction. Submissions must clearly articulate the impact of data science on the materials problem of interest to be considered.
Sponsor:
Computational Molecular Science and Engineering Forum
Co-Sponsor(s):
Thermodynamics and Transport Properties (01A), Information Management and Intelligent Systems (10E)
Chair:
Stephanie Valleau
Chemical Engineering, University of Washington, Seattle, WA
Co-chairs:
Yi He
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
Qing Shao
Chemical and Materials Engineering, University of Kentucky, Lexington, KY
Abstract Submissions Closed
See more of this Group/Topical: Computational Molecular Science and Engineering Forum