Machine Learning Optimization of p-Type Transparent Conducting Films
暂无分享,去创建一个
Xiaojie Xu | Joel W. Ager | James Bullock | J. Ager | J. Bullock | Lingfei Wei | Xiaojie Xu | Gurudayal | Lingfei Wei
[1] C. Lokhande,et al. Chemical deposition method for metal chalcogenide thin films , 2000 .
[2] K. Nemade,et al. Band gap engineering of CuS nanoparticles for artificial photosynthesis , 2015 .
[3] Bernard W. Silverman,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[4] Lin-wang Wang,et al. Copper‐alloyed ZnS as a p‐type transparent conducting material , 2012 .
[5] Chih-Jen Lin,et al. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM , 2010, J. Mach. Learn. Res..
[6] B. Uberuaga,et al. Using Machine Learning To Identify Factors That Govern Amorphization of Irradiated Pyrochlores , 2016, 1607.06789.
[7] Feng Lin,et al. Machine Learning Directed Search for Ultraincompressible, Superhard Materials. , 2018, Journal of the American Chemical Society.
[8] Klaus Hinkelmann,et al. Design and Analysis of Experiments: Special Designs and Applications , 2012 .
[9] S. Chand,et al. Copper thiocyanate (CuSCN): an efficient solution-processable hole transporting layer in organic solar cells , 2015 .
[10] Carl M. Lampert,et al. Editorial: Reporting solar cell efficiencies in Solar Energy Materials and Solar Cells , 2008 .
[11] K. Sankaranarayanan,et al. Effect of EDTA concentration on the physical and optical properties of Cds thin films , 2013 .
[12] Mu-Chen Chen,et al. Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..
[13] Hebert Montegranario,et al. Radial Basis Functions , 2014 .
[14] Gene H. Golub,et al. Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.
[15] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[16] J. Moon,et al. Effect of pH on the characteristics of nanocrystalline ZnS thin films prepared by CBD method in acidic medium , 2010 .
[17] M. Ichimura,et al. Heterojunctions based on photochemically deposited CuxZnyS and electrochemically deposited ZnO , 2015 .
[18] Lukas Turcani,et al. Machine Learning for Organic Cage Property Prediction , 2019 .
[19] J. Buriak,et al. Reporting performance in organic photovoltaic devices. , 2013, ACS nano.
[20] G. Lavareda,et al. Optoelectronic properties of transparent p‐type semiconductor CuxS thin films , 2010 .
[21] p-Type Zinc Oxide Spinels: Application to Transparent Conductors and Spintronics , 2013, 1312.1728.
[22] L. A. González,et al. p-Type transparent Cu doped ZnS thin films by the chemical bath deposition method , 2014 .
[23] Paul Raccuglia,et al. Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.
[24] A First Course in Design and Analysis of Experiments , 2003 .
[25] Xiangyang Ma,et al. Effects of complexing agent on CdS thin films prepared by chemical bath deposition , 2004 .
[26] Taylor D. Sparks,et al. High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds , 2016 .
[27] C. Granqvist. Transparent conductors as solar energy materials: A panoramic review , 2007 .
[28] R. Waser,et al. Chemical Solution Deposition of Functional Oxide Thin Films , 2013 .
[29] R. Egdell,et al. P-type transparent conducting oxides , 2016, Journal of physics. Condensed matter : an Institute of Physics journal.
[30] C. Ballif,et al. Transparent Electrodes for Efficient Optoelectronics , 2017 .
[31] Margaret J. Robertson,et al. Design and Analysis of Experiments , 2006, Handbook of statistics.
[32] V. Pecharsky,et al. Magnetocaloric Behavior in Ternary Europium Indides EuT5In: Probing the Design Capability of First-Principles-Based Methods on the Multifaceted Magnetic Materials , 2017 .
[33] Michele Pavone,et al. First-Principles Design of New Electrodes for Proton-Conducting Solid-Oxide Electrochemical Cells: A-Site Doped Sr2Fe1.5Mo0.5O6−δ Perovskite , 2016 .
[34] M. Toney,et al. Chemical Bath Deposition of p-Type Transparent, Highly Conducting (CuS)x:(ZnS)1-x Nanocomposite Thin Films and Fabrication of Si Heterojunction Solar Cells. , 2016, Nano letters.
[35] Guifeng Li,et al. p-type transparent conductor: Zn-doped CuAlS2 , 2007 .
[36] K. Ellmer. Past achievements and future challenges in the development of optically transparent electrodes , 2012, Nature Photonics.
[37] G. Haacke. New figure of merit for transparent conductors , 1976 .
[38] J. S. Hunter,et al. Statistics for experimenters : an introduction to design, data analysis, and model building , 1979 .
[39] D. Ginley,et al. Handbook of transparent conductors , 2011 .
[40] T. Simpson,et al. Analysis of support vector regression for approximation of complex engineering analyses , 2005, DAC 2003.
[41] Giulia Galli,et al. Perovskites for Solar Thermoelectric Applications: A First Principle Study of CH3NH3AI3 (A = Pb and Sn) , 2014 .
[42] Kristin A. Persson,et al. Electrochemical Stability of Metastable Materials , 2017 .
[43] D. Keszler,et al. Transparent p-type conducting BaCu2S2 films , 2002 .
[44] M. Ichimura,et al. Electrochemical Deposition of CuxS and CuxZnyS Thin Films with p-Type Conduction and Photosensitivity , 2012 .
[45] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[46] Lawrence A. Adutwum,et al. How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics. , 2018, ACS nano.
[47] Tony Gray. Transparent Conductors , 2018, Projected Capacitive Touch.
[48] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[49] V. Ozoliņš,et al. Increasing the thermoelectric figure of merit of tetrahedrites by Co-doping with nickel and zinc , 2015 .
[50] Daniel W. Davies,et al. Machine learning for molecular and materials science , 2018, Nature.
[51] Erik Johansson,et al. Generalized Subset Designs in Analytical Chemistry. , 2017, Analytical chemistry.
[52] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[53] Ryan P. Adams,et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. , 2016, Nature materials.
[54] Anjali Kshirsagar,et al. Photophysical properties of ZnS nanoclusters , 2000 .
[55] A. Javey,et al. Solution‐Processed Transparent Self‐Powered p‐CuS‐ZnS/n‐ZnO UV Photodiode , 2018 .
[56] C. Choi,et al. Chemical bath deposited ZnS buffer layer for Cu(In,Ga)Se 2 thin film solar cell , 2018 .
[57] Tin Kam Ho,et al. A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors , 2002, Pattern Analysis & Applications.
[58] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[59] Swanti Satsangi,et al. Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene , 2018, Chemistry of Materials.
[60] B. Efron. Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation , 1983 .
[61] A. Subrahmanyam,et al. p-type transparent conducting In2O3-Ag2O thin films prepared by reactive electron beam evaporation technique , 2000 .
[62] H. Ohta,et al. Degenerate p-type conductivity in wide-gap LaCuOS1−xSex (x=0–1) epitaxial films , 2003 .
[63] D. Lim,et al. Characterization of ZnS Thin Films Grown Using Chemical Bath Deposition with Three Different Complexing Agents. , 2018, Journal of nanoscience and nanotechnology.
[64] Wenjun Yang,et al. Synthesis, Characterization, and Biological Application of Size-Controlled Nanocrystalline NaYF4:Yb,Er Infrared-to-Visible Up-Conversion Phosphors , 2004 .
[65] B. Silverman. Density estimation for statistics and data analysis , 1986 .
[66] I. Sharp,et al. P‐Type Transparent Cu‐Alloyed ZnS Deposited at Room Temperature , 2016 .
[67] K. Fleischer,et al. Quantifying the Performance of P-Type Transparent Conducting Oxides by Experimental Methods , 2017, Materials.
[68] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[69] A. Draeseke,et al. p-Type conductivity in the delafossite structure , 2001 .
[70] Fuqiang Huang,et al. p-Type electrical conduction and wide optical band gap in Mg-doped CuAlS2 , 2008 .
[71] Leif E. Peterson. K-nearest neighbor , 2009, Scholarpedia.
[72] Mauricio Ortega-López,et al. Improved efficiency of the chemical bath deposition method during growth of ZnO thin films , 2003 .
[73] Jingjing Xu,et al. Enhanced Figure-of-Merit in Se-Doped p-Type AgSbTe2 Thermoelectric Compound , 2010 .
[74] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[75] O. Conde,et al. Transparent p-type CuxS thin films , 2011 .
[76] H. Angerer,et al. Thermopower investigation of n- and p-type GaN , 1998 .
[77] Jakoah Brgoch,et al. Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC. , 2017, Journal of the American Chemical Society.
[78] Bernhard Schölkopf,et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..
[79] M. Ichimura,et al. Photochemical deposition of a p-type transparent alloy semiconductor CuxZnyS , 2012 .
[80] Cheng-Lung Huang,et al. A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..
[81] J. Ager,et al. High figure-of-merit p-type transparent conductor, Cu alloyed ZnS via radio frequency magnetron sputtering , 2017 .
[82] Gian-Marco Rignanese,et al. High-Throughput Design of Non-oxide p-Type Transparent Conducting Materials: Data Mining, Search Strategy, and Identification of Boron Phosphide , 2017 .
[83] Akihiko Kudo,et al. Photocatalytic H2 evolution under visible light irradiation on Zn1-xCuxS solid solution , 1999 .
[84] Yunqian Ma,et al. Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.
[85] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[86] David W. Hosmer,et al. Applied Logistic Regression , 1991 .
[87] Jeong Yong Lee,et al. Preparation and characteristics of chemical bath deposited ZnS thin films: Effects of different complexing agents , 2012 .