High-throughput and Data-driven Approaches Guiding Smart Operando Experiments (Workshop 3)

Tuesday 22 May 2018, 13:00 → 18:30 US/Eastern

Berkner Hall, Bldg. 488, Conf. Rm. B

In recent years, considerable progress has been made in operando studies combining high-throughput experimental and computational methodologies with data-mining techniques. Building on the fast growing scientific databases such as the Materials Project, the enhanced computing capabilities, and the development of high throughput X-ray facilities for spectroscopy, scattering and imaging, researchers can obtain key morphological, structural, and electronic information that is essential for the understanding of atomic-level details of dynamic processes. Once the thermodynamic and kinetic pathways governing material performance are well understood, the synthetic parameters and reaction conditions can be tuned through in-line operando control paired with computational feedback. This will enable the capabilities to optimize the experimental conditions in order to manipulate these processes in a controlled way.
 
The workshop will bring the important players from the three fields together to document state-of-the-art developments in each of the these areas, emphasizing examples of successful implementation of high throughput experiment-theory-data synergy as well as devising strategies to tackle emerging roadblocks. Finally, the workshop will exemplify how smart operando experiments, including but not limited to X-ray based screening techniques, were used to solve difficult materials optimization problems in inhomogeneous  systems on multiple length scales.
 
In addition to the talks on May 22, the workshop will also provide a half day tutorial on the use of the Materials Project (MP) on May 23. In this tutorial, the participants will have hands-on opportunity to learn how to use the web interface and core primitives and modules of the MP for materails analysis. Participants of the tutorial will need to bring their own laptop and create an account at the Materials Project in advance, and are encouraged to get familiar with Python and the Jupyter notebook in order to use more advanced functions of the MP.

Tutorial: The Materials Project Hands-on Tutorial on Data Mining Materials Structures and Properties
Instructor: Patrick Huck (Lawrence Berkeley National Laboratory )
Date: Wednesday, May 23, 2018
Time: 1:00 - 5:00 pm
Location: Medical Bldg. 490 - Large Conference Room
  
Organizers: Deyu Lu (BNL), Klaus Attenkofer (BNL), Eli Stavitski (BNL)

Back to Main Agenda

Workshop_Summary.docx

1 Opening Remarks

2 Product and Process Analysis by High-throughput X-ray Absorption Spectroscopy: From Concept to Reality

Speaker: Sven L. M. Schroeder, University of Leeds

3 An Iterative Machine Learning – High-throughput Experimental Approach to Discovering Novel Amorphous Alloys

Speaker: Jason Hattrick-Simpers, National Institute of Standards and Technology

4 Machine Learning for Automated X-ray Scattering Experiments

Speaker: Kevin Yager, Center for Functional Nanomaterials

5 Extracting Nanoscale Details from X-ray Absorption Data by Supervised Machine Learning

Speaker: Janis Timoshenko, Stony Brook University

6 Coffee Break (Included) and Group Photo

7 Combining Atomistic Modeling and Machine Learning for Co-refinement of x-ray and Electron Characterization Data

Speaker: Maria Chan, Argonne National Laboratory

8 X-ray Spectroscopy Theory, Data Base Mining, and Bayesian Analysis

Speaker: John Rehr, University of Washington

9 Prospects for Elucidating Reaction Mechanisms via Adaptive Transient Kinetics Experiments

Speaker: Andrew Medford, Georgia Tech

10 Coffee Break

11 The Materials Project: Conception to Confirmation in a Virtual Lab

Speaker: Shyam Dwaraknath, Lawrence Berkeley National Laboratory

12 Data Management and Data-Enabled Research at NIST

Speaker: Robert Hanisch, National Institute of Standards and Technology

13 Provenance-enabled Sample Measurements and Tracking for Multi-modal Analysis and Predictive Synthesis

Speaker: Line Pouchard, Computational Science Initiative

14 Summary and Discussion