A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
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Seiji Kajita | Nobuko Ohba | Ryosuke Jinnouchi | Ryoji Asahi | R. Asahi | Seiji Kajita | R. Jinnouchi | N. Ohba
[1] Van Vechten,et al. Quantum Dielectric Theory of Electronegativity in Covalent Systems. I. Electronic Dielectric Constant , 1969 .
[2] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[3] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .
[4] G. B. Olson,et al. Computational Design of Hierarchically Structured Materials , 1997 .
[5] Gábor Csányi,et al. Accuracy and transferability of Gaussian approximation potential models for tungsten , 2014 .
[6] Felix A Faber,et al. Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. , 2015, Physical review letters.
[7] Klaus H. Maier-Hein,et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping , 2016, NeuroImage.
[8] Shuichi Iwata,et al. Data-Driven Atomic Environment Prediction for Binaries Using the Mendeleev Number. Part 1. Composition AB. , 2004 .
[9] Gábor Csányi,et al. Comparing molecules and solids across structural and alchemical space. , 2015, Physical chemistry chemical physics : PCCP.
[10] Burke,et al. Generalized Gradient Approximation Made Simple. , 1996, Physical review letters.
[11] Ming Yang,et al. 3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Alex Zunger,et al. Systematization of the stable crystal structure of all AB-type binary compounds: A pseudopotential orbital-radii approach , 1980 .
[13] M. Rupp,et al. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties , 2013, 1307.2918.
[14] R. Parr. Density-functional theory of atoms and molecules , 1989 .
[15] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[16] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[17] Alexander Tropsha,et al. Materials Informatics , 2019, J. Chem. Inf. Model..
[18] Stéphane Mallat,et al. Wavelet Scattering Regression of Quantum Chemical Energies , 2016, Multiscale Model. Simul..
[19] Ekin D. Cubuk,et al. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials , 2017 .
[20] Steven G. Louie,et al. Nonlinear ionic pseudopotentials in spin-density-functional calculations , 1982 .
[21] K. Müller,et al. Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.
[22] R. Kondor,et al. On representing chemical environments , 2012, 1209.3140.
[23] B. Meredig,et al. Materials science with large-scale data and informatics: Unlocking new opportunities , 2016 .
[24] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] J. A. Van Vechten,et al. Quantum Dielectric Theory of Electronegativity in Covalent Systems. II. Ionization Potentials and Interband Transition Energies , 1969 .
[26] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[27] Mark E. Oxley,et al. Binary, ternary and quaternary compound former/nonformer prediction via Mendeleev number , 2001 .
[28] Alexie M. Kolpak,et al. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods , 2017, Scientific Reports.
[29] Tianqi Chen,et al. Empirical Evaluation of Rectified Activations in Convolutional Network , 2015, ArXiv.
[30] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[31] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[32] Phillip B. Messersmith,et al. Bioinspired antifouling polymers , 2005 .
[33] Blöchl,et al. Projector augmented-wave method. , 1994, Physical review. B, Condensed matter.
[34] R. Martin,et al. Electronic Structure: Basic Theory and Practical Methods , 2004 .
[35] Nongnuch Artrith,et al. An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2 , 2016 .
[36] Richard M. Martin. Electronic Structure: Frontmatter , 2004 .
[37] S. Ong,et al. New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships , 2016 .
[38] Atsuto Seko,et al. Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization. , 2015, Physical review letters.
[39] Sebastian Scherer,et al. VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[40] Felix A Faber,et al. Crystal structure representations for machine learning models of formation energies , 2015, 1503.07406.
[41] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[42] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[43] Kresse,et al. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. , 1996, Physical review. B, Condensed matter.
[44] Andrew Y. Ng,et al. Convolutional-Recursive Deep Learning for 3D Object Classification , 2012, NIPS.
[45] Jörg Behler,et al. Constructing high‐dimensional neural network potentials: A tutorial review , 2015 .