최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Npj Computational materials, v.7 no.1, 2021년, pp.106 -
Na, Gyoung S. , Jang, Seunghun , Chang, Hyunju
AbstractDopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials. In particular, the dopants are essential to improve thermoelectic performances of the materials. However, existing machine learning methods cannot accurately predict th...
npj Comput. Mater. X-P Wang 6 31 2020 10.1038/s41524-020-0303-z Wang, X.-P. et al. Time-dependent density-functional theory molecular-dynamics study on amorphization of sc-sb-te alloy under optical excitation. npj Comput. Mater. 6, 31 (2020).
ACS Omega Y-C Tsai 5 3917 2020 10.1021/acsomega.9b03353 Tsai, Y.-C. & Bayram, C. Band alignments of ternary wurtzite and zincblende iii-nitrides investigated by hybrid density functional theory. ACS Omega 5, 3917-3923 (2020).
RSC Adv. S Jang 5 39319 2015 10.1039/C5RA04350F Jang, S. et al. First-principles calculation of metal-doped caalsin3: material design for new phosphors. RSC Adv. 5, 39319-39323 (2015).
Sci. Rep. P Umari 4 2014 10.1038/srep04467 Umari, P., Mosconi, E. & Angelis, F. D. Relativistic GW calculations on CH3NH3PbI3 and CH3NH3SnI3 perovskites for solar cell applications. Sci. Rep. 4, 4467 (2014).
J. Chem. Theory Comput. M Govoni 11 2680 2015 10.1021/ct500958p Govoni, M. & Galli, G. Large scale gw calculations. J. Chem. Theory Comput. 11, 2680-2696 (2015).
Phys. Rev. B J Shim 71 035206 2005 10.1103/PhysRevB.71.035206 Shim, J., Lee, E.-K., Lee, Y. J. & Nieminen, R. M. Density-functional calculations of defect formation energies using supercell methods: defects in diamond. Phys. Rev. B 71, 035206 (2005).
J. Phys. Chem. Lett Y Zhuo 9 1668 2018 10.1021/acs.jpclett.8b00124 Zhuo, Y., Mansouri Tehrani, A. & Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J. Phys. Chem. Lett 9, 1668-1673 (2018).
Phys. Rev. Lett. T Xie 120 145301 2018 10.1103/PhysRevLett.120.145301 Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).
Chem. Mater. Z-W Zhao 32 7777 2020 10.1021/acs.chemmater.0c02325 Zhao, Z.-W., del Cueto, M., Geng, Y. & Troisi, A. Effect of increasing the descriptor set on machine learning prediction of small molecule-based organic solar cells. Chem. Mater. 32, 7777-7787 (2020).
Phys. Rev. B J Lee 93 115104 2016 10.1103/PhysRevB.93.115104 Lee, J., Seko, A., Shitara, K., Nakayama, K. & Tanaka, I. Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques. Phys. Rev. B 93, 115104 (2016).
Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR) (2017).
Chem. Sci. Z Wu 9 513 2018 10.1039/C7SC02664A Wu, Z. et al. Moleculenet: a benchmark for molecular machine learning. Chem. Sci. 9, 513-530 (2018).
J. Comput. Aided Mol. Des. T Morawietz 35 557 2020 10.1007/s10822-020-00346-6 Morawietz, T. & Artrith, N. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications. J. Comput. Aided Mol. Des. 35, 557-586 (2020).
Nat. Mater. A Zitolo 14 937 2015 10.1038/nmat4367 Zitolo, A. et al. Identification of catalytic sites for oxygen reduction in iron- and nitrogen-doped graphene materials. Nat. Mater. 14, 937-942 (2015).
Sci. Adv. J Shui 1 1 2015 10.1126/sciadv.1400129 Shui, J., Wang, M., Du, F. & Dai, L. N-doped carbon nanomaterials are durable catalysts for oxygen reduction reaction in acidic fuel cells. Sci. Adv. 1, 1-7 (2015).
J. Phys. Chem. Lett. S Das Adhikari 10 2250 2019 10.1021/acs.jpclett.9b00182 Das Adhikari, S., Guria, A. K. & Pradhan, N. Insights of doping and the photoluminescence properties of mn-doped perovskite nanocrystals. J. Phys. Chem. Lett. 10, 2250-2257 (2019).
Adv. Mater. Y Pei 24 6125 2012 10.1002/adma.201202919 Pei, Y., Wang, H. & Snyder, G. J. Band engineering of thermoelectric materials. Adv. Mater. 24, 6125-6135 (2012).
J. Mater. Sci. J Wei 55 12642 2020 10.1007/s10853-020-04949-0 Wei, J. et al. Review of current high-zt thermoelectric materials. J. Mater. Sci. 55, 12642-12704 (2020).
J. Mater. Chem. SK Bux 21 12259 2011 10.1039/c1jm10827a Bux, S. K. et al. Mechanochemical synthesis and thermoelectric properties of high quality magnesium silicide. J. Mater. Chem. 21, 12259-12266 (2011).
Appl. Phys. Lett. S Sakurada 86 082105 2005 10.1063/1.1868063 Sakurada, S. & Shutoh, N. Effect of ti substitution on the thermoelectric properties of (zr,hf)nisn half-heusler compounds. Appl. Phys. Lett. 86, 082105 (2005).
Neural Netw. A Tavanaei 111 47 2019 10.1016/j.neunet.2018.12.002 Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T. & Maida, A. Deep learning in spiking neural networks. Neural Netw. 111, 47-63 (2019).
IEEE Trans. Neural Netw. Learn. Syst. W Bian 25 545 2014 10.1109/TNNLS.2013.2278427 Bian, W. & Chen, X. Neural network for nonsmooth, nonconvex constrained minimization via smooth approximation. IEEE Trans. Neural Netw. Learn. Syst.25, 545-556 (2014).
Weinberger, K. Q., Blitzer, J. & Saul, L. K. Distance metric learning for large margin nearest neighbor classification. In Conference on Neural Information Processing Systems (NIPS) (MIT Press, 2009).
Chem. Mater. MW Gaultois 25 2911 2013 10.1021/cm400893e Gaultois, M. W. et al. Data-driven review of thermoelectric materials: performance and ressource considerations. Chem. Mater. 25, 2911-2920 (2013).
J. Mach. Learn. Res. L van der Maaten 9 2579 2008 van der Maaten, L. & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579-2605 (2008).
Baldi, P. Autoencoders, unsupervised learning and deep architectures. In Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop - Volume 27, UTLW’11, 37-50 (JMLR.org, 2011).
Renew. Sust. Energy Rev. C Forman 57 1568 2016 10.1016/j.rser.2015.12.192 Forman, C., Muritala, I., Pardemann, R. & Meyer, B. Estimating the global waste heat potential. Renew. Sust. Energy Rev. 57, 1568-1579 (2016).
Ann. Phys. T Seebeck 82 133 1826 10.1002/andp.18260820202 Seebeck, T. Ueber die magnetische polarisation der metalle und erze durch temperatur-diferenz. Ann. Phys. 82, 133-160 (1826).
Nat. Mater. GJ Snyder 7 105 2008 10.1038/nmat2090 Snyder, G. J. & Toberer, E. S. Complex thermoelectric materials. Nat. Mater. 7, 105-114 (2008).
10.1007/s40243-020-00175-5 Julio Gutiérrez Moreno, J., Cao, J., Fronzi, M. & Assadi, M.H.N. A review of recent progress in thermoelectric materials through computational methods. Mater. Renew. Sustain. Energy 9, 16 (2020).
Nature Y LeCun 521 436 2015 10.1038/nature14539 LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015).
Wilson, A. G. & Adams, R. P. Gaussian process kernels for pattern discovery and extrapolation. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, ICML’13 (JMLR.org, 2013).
Nano Energy Z Wang 81 105665 2021 10.1016/j.nanoen.2020.105665 Wang, Z., Zhang, H. & Li, J. Accelerated discovery of stable spinels in energy systems via machine learning. Nano Energy 81, 105665 (2021).
J. Chem. Inf. Model. RP Sheridan 56 2353 2016 10.1021/acs.jcim.6b00591 Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J. & Gifford, E. M. Extreme gradient boosting as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 56, 2353-2360 (2016).
Nature D Rothschild 555 210 2018 10.1038/nature25973 Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210-215 (2018).
IEEE Acess D Zhang 6 21020 2018 10.1109/ACCESS.2018.2818678 Zhang, D. et al. A data-driven design for fault detection of wind turbines using random forests and xgboost. IEEE Acess 6, 21020-21031 (2018).
J. Am. Chem. Soc. P Jood 142 15464 2020 10.1021/jacs.0c07067 Jood, P. et al. Na doping in pbte: solubility, band convergence, phase boundary mapping, and thermoelectric properties. J. Am. Chem. Soc. 142, 15464-15475 (2020).
Int. J. Energy Res. MN Hasan 44 6170 2020 10.1002/er.5313 Hasan, M. N., Wahid, H., Nayan, N. & Mohamed Ali, M. S. Inorganic thermoelectric materials: a review. Int. J. Energy Res. 44, 6170-6222 (2020).
Xu, K. et al. How neural networks extrapolate: From feedforward to graph neural networks. In International Conference on Learning Representations (2021).
RSC Adv. T Fan 8 17168 2018 10.1039/C8RA02436G Fan, T., Xie, C., Wang, S., Oganov, A. R. & Cheng, L. First-principles study of thermoelectric properties of Mg2Si-Mg22Pb semiconductor materials. RSC Adv. 8, 17168-17175 (2018).
Mater. Horiz. J-H Pőhls 8 209 2021 10.1039/D0MH01112F Pőhls, J.-H. et al. Experimental validation of high thermoelectric performance in RECuZnP2 predicted by high-throughput dft calculations. Mater. Horiz. 8, 209-215 (2021).
Phys. Rev. W Kohn 140 A1133 1965 10.1103/PhysRev.140.A1133 Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133-A1138 (1965).
Kingma, D. P. & Welling, M. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR) (2014).
J. Mach. Learn. Res. N Srivastava 15 1929 2014 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958 (2014).
Kingma, D. P. & Ba, J. L. Adam: A method for stochastic optimization.In International Conference on Learning Representations (ICLR) (2015).
Python mendeleev package. https://github.com/lmmentel/mendeleev (2020). Accessed 12 March 2021.
Agarap, A. F. Deep learning using rectified linear units (ReLU). Preprint at https://arxiv.org/abs/1803.08375 (2018).
Nature K Biswas 489 414 2012 10.1038/nature11439 Biswas, K. et al. High-performance bulk thermoelectrics with all-scale hierarchical architectures. Nature 489, 414-418 (2012).
Nature Y Pei 473 66 2011 10.1038/nature09996 Pei, Y. et al. Convergence of electronic bands for high performance bulk thermoelectrics. Nature 473, 66-69 (2011).
Chem. Mater. T He 18 759 2006 10.1021/cm052055b He, T., Chen, J., Rosenfeld, H. D. & Subramanian, M. A. Thermoelectric properties of indium-filled skutterudites. Chem. Mater. 18, 759-762 (2006).
Science JP Heremans 321 554 2008 10.1126/science.1159725 Heremans, J. P. et al. Enhancement of thermoelectric efficiency in pbte by distortion of the electronic density of states. Science 321, 554-557 (2008).
해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.