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Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability

Data Science and Optimal Learning for Material Discovery and Design

May 16, 2016 8:00 AM - May 18, 2016 5:00 PM
Hilton Santa Fe
Karla Jackson
(505) 667-5336

Event Description

Accelerating materials discovery has been an emerging theme in several Office of Science and other government reports and proposal calls.

It also has been the topic of several recent scientific workshops. The Materials Genome Initiative is part of a bold plan to boost US manufacturing by halving the time it takes to discover and design new materials. In this plan, accelerating discovery relies on using high performance computing, mathematics and data in the material sciences in a manner similar to the way that they were used to make the Human Genome Initiative a success for the biological sciences. Novel approaches are therefore being called for that can explore the enormous phase space presented by complex materials and processes. If we are to achieve the desired performance gains, we must have a predictive capability that can guide experiments and computations in the most fruitful directions by reducing the possibilities that need to be tried.

In order to meaningfully adapt the spirit of ''genomics'' style research in materials science, we need to first identify and map the ''genes'' across length and time scales. Despite advances in computation and experimental techniques generating vast arrays of data, without a clear link to inference and optimization methods that can constrain predictions using insights and results from theory, the full value of data-driven discovery will not be realized. Given that the field has matured over the last couple of years, the aim of this three-day conference is to address this challenge by bringing together an interdisciplinary group of researchers from the fields of materials and the information sciences to discuss the state of the art, outline the open questions, as well as lay out future directions in the application of information theoretic tools to materials problems related to learning from experimental and computational data, designing new materials and impacting computational models.  

This meeting is supported by the proposed LANL XFEL signature facility concept for mesoscale science, MaRIE, Matter Radiation in Extremes, and is being held under the auspices of the Information Science and Technology Center at Los Alamos National Laboratory (LANL).  

We intend to have sessions focused on assessing the current state-of-art and challenges related to the following themes:
(a) Information-theoretic tools and their application to materials science
(b) Learning from and generating high throughput computational and experimental data
(c) Analysis of data from probes such as light sources, as well as other experiments.

A poster session is planned for Monday evening. Graduate students and post-doctoral fellows are especially encouraged to submit posters/short talks.


J. Bernier (LLNL)
P. Coveney (UCL)
L. Dalton (OSU)
M. De Graf (CMU)
E. Dougherty (TAMU)
B. Jones (IBM)
S. Kalinin (ORNL)
Y.  Marzouk (MIT)
M. Mckerns (Caltech)
R. Sashadri (UCSB)
I. Takeuchi (Maryland)
S. Torquato (Princeton)
L. Varshney (Illinois)
J. Warren (NIST)
N. Zabaras (Warwick)
A. Zunger (Colorado)

If interested or for more information, see the agenda and register online.

Organizing Committee:

Turab Lookman
Francis J. Alexander
Stephan Eidenbenz

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