The Air Force Research Laboratory (AFRL), in partnership with the National Institute of Standards and Technology (NIST) and the National Science Foundation (NSF), have announced the winners of the Materials Science and Engineering Data Challenge. The Challenge sought solutions for new uses of publicly accessible digital data to advance materials science and engineering knowledge to accelerate the transition to industrial applications.
The top prize was awarded to the team of Joshua Gomberg, Andrew Medford, and Surya Kalidindi from the Georgia Institute of Technology. Their project, “Structure-based energy models from simulated Al grain boundary datasets,” proposes new methods to extract information from atomistic simulations to predict the energy of defects in materials – knowledge that is critical to understanding material properties for application.
“The submissions selected for award spanned scientific and engineering application; from prediction of new crystal structures to forecasting the behavior of titanium airfoils in jet engines. The Challenge demonstrated that publicly available materials data has the potential for reuse to solve important technology problems,” said Dr. Charles Ward, AFRL’s Integrated Computational Materials Science and Engineering Lead.
The Challenge was formed to further the goals of the Materials Genome Initiative, an effort launched by the White House in 2011 to guide the nation’s efforts in reducing time and cost in bringing new materials and manufacturing products to market. Areas of interest include the discovery of new materials to meet an application need, or development of a new model describing processing-structure-property relationships in either a structural (load bearing), functional (electrical, optical or magnetic), or multifunctional material. To participate in the challenge, participants were required to submit a research report in a format suitable for a peer-reviewed scientific publication. Multiple proposals were selected to receive funding, with the top performer receiving $25,000.
Dr. James Warren, Director of the Materials Genome Program at NIST, noted that “the Challenge proved that great materials science can be done using open data, while simultaneously pointing the way towards more reproducible, more broadly usable research.” Dr. Alexis Lewis, Program Director for Materials Engineering and Processing and DMREF at NSF, added that “the exciting results from this Challenge demonstrate that efforts to make research data publicly available can have tremendous payoff.”
Winners are invited to present their work at a special session during the Materials Science and Technology Conference held October 24-27, 2016, in Salt Lake City, Utah.
The additional Challenge winners are:
Wenhao Sun, Stephen Dacek, and Will Richards from the Massachusetts Institute of Technology; Shyue Ping Ong, Anthony C. Gamst, and Kristin A. Persson from the University of California; Geoffroy Hautier from Université Catholique de Louvain, Louvain-la-Neuve; and Anubhav Jain and Gerbrand Ceder from Lawrence Berkeley National Laboratory for their effort titled “The Thermodynamic Scale of Inorganic Crystalline Metastability”.
Anton O. Oliynyk and Arthur Mar from , the University of Alberta, Canada; Erin Antono and Bryce Meredig from Citrine Informatics; Taylor D. Sparks and Leila Ghadbeigi from the University of Utah; and Michael W. Gaultois from University of Cambridge, England, for their work titled “Deceptively Simple and Endlessly Complicated: Machine Learning Prediction and Experimental Confirmation of Novel Heusler Compounds”.
Bruno Abreu Calfa from the University of Wisconsin-Madison and John R. Kitchin from Carnegie Mellon University for their undertaking titled “Optimal Design of Atomic Crystalline Solids using Kernel Regression Property Prediction Models”.
Eva Popova, Xinyi Gong, Ahmet Cecen, and Surya Kalidindi from the Georgia Institute of Technology and Theron Rodgers and Jonathan Madison from Sandia National Laboratory for their work titled “Extraction of Process-Structure Linkages from Simulated Additive Manufacturing Microstructures Using a Data Science Approach”.
Ayman Salem, Joshua Shaffer, Richard Kublik, and Daniel Satko from Materials Resources LLC for their work titled “Towards Shareable Materials Science: Cloud-Based Data-Driven Modeling for Fatigue Life Prediction in Ti-6Al-4V for Turbine Blade Applications”.
Author: Donna Lindner, AFRL Materials and Manufacturing Directorate
POC: Dr. Charles Ward, AFRL Materials and Manufacturing Directorate, email@example.com