Computational Discovery and Design of Emerging Materials: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee
Program Organizers: Arunima Singh, Arizona State University; Houlong Zhuang, Arizona State University; Sugata Chowdhury, National Institute of Standards and Technology; Arun Kumar Mannodi Kanakkithodi, Purdue University
Tuesday 5:30 PM
February 25, 2020
Room: Sails Pavilion
Location: San Diego Convention Ctr
L-15: Discovery of Rare-earth-free Magnetic Materials Using Adaptive Genetic Algorithm and First-principles Calculations: Zejin Yang1; Kai-Ming Ho2; Cai-Zhuang Wang2; 1Zhejiang University of Technology and Iowa State University; 2Iowa State University
Using the adaptive genetic algorithm and first principles calculations, we systematically explored the low-energy structures and magnetic properties of FexSiy compounds over a wide range of chemical compositions, especially focus on Fe-rich compounds. In addition to reproducing the experimentally synthesized structures, 36 new structures with energies less than 50 meV/atom with respect to the convex hull of experimentally known stable structures are obtained. Magnetic properties of these low-energy structures are investigated. Some of these structures are shown to be promising candidates for rare-earth-free permanent magnet.
L-16: Machine Learning Models for the Lattice Thermal Conductivity Prediction of Inorganic Materials: Lihua Chen1; 1Georgia Institute of Technology
The lattice thermal conductivity (k_L) is a critical property of thermoelectrics, thermal barrier coating materials and semiconductors. While accurate empirical measurements of k_L are extremely challenging, it is usually approximated through computational approaches, such as semi-empirical models, Green-Kubo formalism coupled with molecular dynamics simulations, and first-principles based methods. However, these theoretical methods are not only limited in terms of their accuracy, but sometimes become computationally intractable owing to their cost. Thus, in this work, we build a machine learning (ML)-based model to accurately and instantly predict k_L of inorganic materials, using a benchmark data set of experimentally measured k_L of about 100 inorganic materials. We use advanced and universal feature engineering techniques along with the Gaussian process regression algorithm, and compare the performance of our ML model with past theoretical works. The trained ML model is helpful for rational design and screening of novel materials.
L-17: Searching for Electrical Conductivity Tunable Organic Molecules for Single-molecule Electronics: Weiyi Zhang1; Peter Greaney1; 1University of California, Riverside
Single-molecule electronics offer a promising solutions reducing the scale of electronic circuits and the size of electrical devices. In our previous work, we had been able to computationally design organic molecules, and by molecular dynamic simulation obtained a group of flexible molecules which can be mechanically isomerized. Here report By computing electrical conductivity of these flexible molecules at different strain, we seek organic molecules with electrical conductivity that can be switched or tuned by deformation. Such molecules would have potential application in sensing and as single-molecule piezoelectrical devices.