A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
出版年份 2017 全文链接
标题
A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
作者
关键词
-
出版物
Scientific Reports
Volume 7, Issue 1, Pages -
出版商
Springer Nature
发表日期
2017-11-29
DOI
10.1038/s41598-017-17299-w
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
- (2017) Austin D. Sendek et al. Energy & Environmental Science
- Wavelet Scattering Regression of Quantum Chemical Energies
- (2017) Matthew Hirn et al. MULTISCALE MODELING & SIMULATION
- Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
- (2017) Brian Kolb et al. Scientific Reports
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
- (2016) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships
- (2016) Anubhav Jain et al. JOURNAL OF MATERIALS RESEARCH
- Materials science with large-scale data and informatics: Unlocking new opportunities
- (2016) Joanne Hill et al. MRS BULLETIN
- Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
- (2016) Jens Kleesiek et al. NEUROIMAGE
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- (2015) O. Anatole von Lilienfeld et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Constructing high-dimensional neural network potentials: A tutorial review
- (2015) Jörg Behler INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
- (2015) Atsuto Seko et al. PHYSICAL REVIEW LETTERS
- Accuracy and transferability of Gaussian approximation potential models for tungsten
- (2014) Wojciech J. Szlachta et al. PHYSICAL REVIEW B
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- 3D Convolutional Neural Networks for Human Action Recognition
- (2012) Shuiwang Ji et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now