Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers

[1]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[2]  John D. Lafferty,et al.  Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Peter Dalgaard,et al.  Introductory statistics with R , 2002, Statistics and computing.

[4]  Martin L. Green,et al.  Combinatorial study of the crystallinity boundary in the HfO2–TiO2–Y2O3 system using pulsed laser deposition library thin films , 2008 .

[5]  K. Rajan.  Combinatorial Materials Science and EBSD: A High Throughput Experimentation Tool , 2009 .

[6]  Krishna Rajan,et al.  Combinatorial and high-throughput screening of materials libraries: review of state of the art. , 2011, ACS combinatorial science.

[7]  Budget,et al.  Memorandum for the Heads of Executive Departments and Agencies: Open Data Policy--Managing Information as an Asset , 2013 .

[8]  Dianne Cook,et al.  The Generalized Pairs Plot , 2013 .

[9]  Jan Schroers,et al.  Combinatorial development of bulk metallic glasses. , 2014, Nature materials.

[10]  Jennifer L. W. Carter,et al.  A statistical study of the effects of processing upon the creep properties of GRCop-84 , 2015 .

[11]  H. V. Jagadish,et al.  The Materials Commons: A Collaboration Platform and Information Repository for the Global Materials Community , 2016 .

[12]  S. Gorsse,et al.  New strategies and tests to accelerate discovery and development of multi-principal element structural alloys , 2017 .

[13]  Jennifer L. W. Carter,et al.  Effects of Changes in Test Temperature on Tensile Properties and Notched Vs Fatigue Precracked Toughness of a Zr-Based BMG Composite , 2017, Metallurgical and Materials Transactions A.

[14]  Roger H. French,et al.  Physics-Informed Network Models: a Data Science Approach to Metal Design , 2017, Integrating Materials and Manufacturing Innovation.

[15]  L. Weston,et al.  Machine learning the band gap properties of kesterite I2−II−IV−V4 quaternary compounds for photovoltaics applications , 2017, Physical Review Materials.

[16]  Shou-Cheng Zhang,et al.  Learning atoms for materials discovery , 2018, Proceedings of the National Academy of Sciences.

[17]  D. Dimiduk,et al.  Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering , 2018, Integrating Materials and Manufacturing Innovation.

[18]  D. Raabe,et al.  On the grain boundary strengthening effect of boron in γ/γ′ Cobalt-base superalloys , 2018 .

[19]  J. Grossman,et al.  Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes , 2018, ACS central science.

[20]  Turab Lookman,et al.  Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning , 2018, Nature Communications.

[21]  Y. Imamura,et al.  Alternative materials for perovskite solar cells from materials informatics , 2019, Physical Review Materials.

[22]  Laura S. Bruckman,et al.  Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels , 2019, Metallurgical and Materials Transactions A.

[23]  Laura S. Bruckman,et al.  Screening of heritage data for improving toughness of creep-resistant martensitic steels , 2019, Materials Science and Engineering: A.

[24]  Christine E Heckle,et al.  Frontiers of Materials Research: A Decadal Survey , 2019 .

[25]  Katy Börner,et al.  Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments , 2019, Proceedings of the National Academy of Sciences.