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Computational Discovery of Two-Dimensional Materials with a Genetic Algorithm for Structure Prediction Benjamin Revard1,2, Arunima Singh3, Rohit Ramanathan1, Michael Ashton2, Richard G. Hennig1,2 1 Department of Materials Science and Engineering, Cornell University, Ithaca, NY, 14850, U.S.A. Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32611, U.S.A. 3 Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, U.S.A. 2 Enthalpy (eV/atom) Materials exhibiting periodicity in fewer than three dimensions have become increasingly important in modern technology due to their unique properties. Low-dimensional materials can exhibit unexpected structures, and in some cases, the preferred crystal structure of a low-dimensional material is quite different from the structure of its bulk counterpart. To address the problem of predicting the crystal structures of lowdimensional materials, we have developed a grand-canonical genetic algorithm for (a) 2D Sn-O System (b) 2D Pb-O System capable of searching structure prediction for (i) 0.2 structures with periodicity in zero, one,(i)two and three dimensions [1]. We 0.0 0.0 apply the algorithm, coupled with −0.2 density-functional methods, to search for −0.5 single-layer materials in the InP, Sn-S −0.4 and C-Si systems [1], and also in the −0.6 group-IV dioxides AO2 (A = Si, Ge, Sn, −1.0 −0.8 (iii) several Pb) [2]. Our searches uncover (ii) novel 2D2Dstructures of InP, as well as −1.0 2D GA GA −1.5 (iii) low-energy Si defects in graphene and Bulk Bulk (ii) −1.2 new 2D 0.2 group-IV dioxide materials. In1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.4 0.6 0.8 we find GeO to exhibit 2 Sn O particular, Ofraction Ofra ction Pb O Figure 1. Predicted phase diagram of 2D Sn-O system. several nearly degenerate 2D phases with low formation energies. We computationally characterize the dynamic and environmental stability and electronic structure of these new 2D group-IV dioxide materials and reveal them to be promising candidates for gate oxides in nanoelectronic devices. References: [1] Revard, B. C., Tipton, W. W., Yesypenko, A., & Hennig, R. G. (2016). Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials. Physical Review B, 93(5), 054117. [2] Singh, A. K., Revard, B. C., Ramanathan, R., Ashton, M., Tavazza, F., & Hennig, R. G. (2017). Genetic algorithm prediction of two-dimensional group-IV dioxides for dielectrics. In print.