Description
Date/Time: 04-03-2018 - Tuesday - 05:00 PM - 07:00 PM
Chia-Yung Jui1 Wen-Jay Lee1 A. C. Yang1 Nan-Yow Chen1 E-Wen Huang2 Nien-Ti Tsou2 An-Chou Yeh3

1, National Applied Research Laboratories, Taichung, , Taiwan
2, National Chiao Tung University, Hsinchu, , Taiwan
3, National Tsing Hua University, Hsinchu, , Taiwan

Exploring novel materials is always the most important issue for product application in industry because it is difficult and cost consuming. Although the rapid development of computational physics and chemistry enables us to calculate the fundamental property of materials, it is still a big challenge to explore the appropriate and unknown materials with good properties for the application, out of thousands of candidates.Due to the achievement of Material genome initiative first launched by U.S. in 2011 and similar projects by other countries worldwide, several huge databases have been created recently. For example, AFlow(organic & inorganic material)[1], Material Project (organic & inorganic material)[2], Khazana (polymer genome)[3], and OQMD (inorganic composites)[4] provides the computational results of material properties by density function theory and machine learning. The huge database has led to the possibility of material design by applying material informatics. In this work, we have employed artificial neural network (ANN) with the database of Material Project to predict the mechanical properties (ie. Young’s modulus, shear modulus, elastic constant) of inorganic compounds. Elemental property (ie. Mass, row & group number in periodic table, atomic number, …), structural(crystal system), and composite (element fraction) features are considered as the descriptors in ANN. After the training and validation of the ANN model, it is validated that using the trained model with the dataset of the binary compound is able to predict the k(>2)-nary compounds. The model has been used to explore the candidate of complex high entropy alloys with high strength. The design of ANN model enables the researchers to screen the material with desired properties from vast compounds.

References
[1]Stefano Curtarolo et al., AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations, Computational Materials Science 58 (2012) 227-235 (http://aflowlib.org/)
[2] A. Jain et al., “The Materials Project: A materials genome approach to accelerating materials innovation” APL Materials 1, 011002 (2013) (https://www.materialsproject.org/)
[3] T. D. Huan et al., A polymer dataset for accelerated property prediction and design, Scientific Data 3 (2016) 160012 (http://khazana.uconn.edu/index.php?m=1)
[4] S. Kirklin et al., The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies, npj Computational Materials 1 (2015) 15010 (http://oqmd.org/)

Meeting Program
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5:00 PM–7:00 PM Apr 3, 2018

PCC North, 300 Level, Exhibit Hall C-E