SPEAKERS

Irene Beyerlein
Matt Challcombe
Scott Dilliard
Volker Eyert
Jim Gubernatis
Jason Johnson
Mo Li
Rod McCabe
James McGuffin-Cawley
Hariharan Naryanan
Katherine Page
Lakshman Prasad
Krishna Rajan
Cynthia Reichhardt
Clint Scovel
Steve Sintay
Ingo Steinwert
Joanne Wendelberger
Brian Williams
Lenka Zdeborova

November 9-10, 2009  |  Los Alamos, New Mexico, USA

A powerful new framework for accelerating materials discovery and design can be developed by combining computational information science techniques, such as statistical learning, with classical computational and experimental materials science techniques.
The purpose of this workshop is to explore how to interface information science and materials science to advance knowledge discovery for materials.  The meeting will examine how to advance materials theory and design by integrating machine learning, statistical physics and data intensive experimentation. It will bring together scientists and engineers specializing in both areas, and focus on identifying areas of opportunity in both fields. Some of the topic areas to be covered include:

• Mathematics and computational foundations  in statistical learning
• Statistics for materials experimentation
• Computational physics and optimization
• Informatics, modeling and materials design

The participants in the workshop will help create a roadmap for developing a robust and accessible knowledge discovery process for materials research using methods from informatics and data driven modeling.  This will include identifying areas of research required in the computer science / applied mathematics community that can help to address pressing needs in materials science.  The meeting will also highlight key materials discovery and design challenges that need or can be significantly advanced by this knowledge discovery process.