Predicting Young's modulus of oxide glasses with sparse datasets using machine learning
暂无分享,去创建一个
Mathieu Bauchy | Nitya Nand Gosvami | N. M. Anoop Krishnan | Hariprasad Kodamana | Sourabh Singh | Suresh Bishnoi | R. Ravinder | H. Kodamana | M. Bauchy | N. Krishnan | R. Ravinder | S. Bishnoi | N. Gosvami | S. Singh
[1] Turab Lookman,et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design , 2019, npj Computational Materials.
[2] M. Micoulaut,et al. Structure and topology of soda-lime silicate glasses: implications for window glass. , 2014, The journal of physical chemistry. B.
[3] Mathieu Bauchy,et al. A new transferable interatomic potential for molecular dynamics simulations of borosilicate glasses , 2018, Journal of Non-Crystalline Solids.
[4] Patrick Huck,et al. Active learning for accelerated design of layered materials , 2018, npj Computational Materials.
[5] Biao Huang,et al. Gaussian process modelling with Gaussian mixture likelihood , 2019, Journal of Process Control.
[6] M. Bauchy,et al. Hardness of silicate glasses: Atomic-scale origin of the mixed modifier effect , 2018, Journal of Non-Crystalline Solids.
[7] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[8] J. Zwanziger,et al. Topological constraints and the Makishima-Mackenzie model , 2015 .
[9] D. Brauer,et al. Solubility of glasses in the system P2O5–CaO–MgO–Na2O–TiO2: Experimental and modeling using artificial neural networks , 2007 .
[10] Turab Lookman,et al. Multi-objective Optimization for Materials Discovery via Adaptive Design , 2018, Scientific Reports.
[11] Lucila Ohno-Machado,et al. Logistic regression and artificial neural network classification models: a methodology review , 2002, J. Biomed. Informatics.
[12] Gabor Csanyi,et al. Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron , 2017, 1706.10229.
[13] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[14] F. Viégas,et al. Deep learning of aftershock patterns following large earthquakes , 2018, Nature.
[15] M. Bauchy,et al. Structural, vibrational, and elastic properties of a calcium aluminosilicate glass from molecular dynamics simulations: the role of the potential. , 2014, The Journal of chemical physics.
[16] Charting the energy landscape of metal/organic interfaces via machine learning , 2017, 1709.05417.
[17] Noam Bernstein,et al. Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential. , 2018, The journal of physical chemistry. B.
[18] Atsuto Seko,et al. Conceptual and practical bases for the high accuracy of machine learning interatomic potential , 2017, 1708.02741.
[19] Xuliang Zhao,et al. 7. , 2020, The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians.
[20] Mathieu Bauchy,et al. Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics Simulations and Machine Learning , 2019, Scientific Reports.
[21] Morten Mattrup Smedskjær,et al. Effect of nanoscale phase separation on the fracture behavior of glasses: Toward tough, yet transparent glasses , 2018, Physical Review Materials.
[22] J V Tu,et al. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. , 1996, Journal of clinical epidemiology.
[23] Haichao Zhu,et al. A New Method to Assist Small Data Set Neural Network Learning , 2006, Sixth International Conference on Intelligent Systems Design and Applications.
[24] J. E. Gubernatis,et al. Machine learning in materials design and discovery: Examples from the present and suggestions for the future , 2018, Physical Review Materials.
[25] Kai Yang,et al. Prediction of Silicate Glasses' Stiffness by High-Throughput Molecular Dynamics Simulations and Machine Learning. , 2019, 1901.09323.
[26] John C. Mauro,et al. Accelerating the Design of Functional Glasses through Modeling: Plenary Talk , 2016 .
[27] A. Varshneya. Fundamentals of Inorganic Glasses , 1993 .
[28] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.
[29] J. Mackenzie,et al. Direct calculation of Young's moidulus of glass , 1973 .
[30] M. Bauchy,et al. Predicting the dissolution kinetics of silicate glasses using machine learning , 2017, 1712.06018.
[31] Sun-Chong Wang,et al. Interdisciplinary Computing in Java Programming , 2003 .
[32] Andrew E Teschendorff,et al. Avoiding common pitfalls in machine learning omic data science , 2018, Nature Materials.
[33] Nima Sammaknejad,et al. Approaches to robust process identification: A review and tutorial of probabilistic methods , 2018, Journal of Process Control.
[34] Carl E. Rasmussen,et al. In Advances in Neural Information Processing Systems , 2011 .
[35] Biao Huang,et al. Mixtures of Probabilistic PCA With Common Structure Latent Bases for Process Monitoring , 2019, IEEE Transactions on Control Systems Technology.
[36] John C. Mauro,et al. Two Centuries of Glass Research: Historical Trends, Current Status, and Grand Challenges for the Future , 2014 .
[37] WerrBn LonwnNsrBrN,et al. THE DISTRIBUTION OF ALUMINUM IN THE TETRAHEDRA OF SILICATES AND ALUMINATES , 2007 .
[38] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[39] Volker L. Deringer,et al. Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory , 2018, Chemistry of Materials.
[40] R. J. Abrahart,et al. Modelling sediment transfer in Malawi: comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets , 2001 .
[41] Markus J. Buehler,et al. De novo composite design based on machine learning algorithm , 2018 .
[42] Noam Bernstein,et al. Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon , 2018, Angewandte Chemie.
[43] Gérard Dreyfus,et al. A machine learning approach to the estimation of the liquidus temperature of glass-forming oxide blends , 2003 .
[44] Pablo G. Debenedetti,et al. Supercooled liquids and the glass transition , 2001, Nature.
[45] J. Mauro. Grand Challenges in Glass Science , 2014, Front. Mater..
[46] Edgar Dutra Zanotto,et al. Predicting glass transition temperatures using neural networks , 2018, Acta Materialia.
[47] Shyue Ping Ong,et al. Accurate Force Field for Molybdenum by Machine Learning Large Materials Data , 2017, 1706.09122.