Deep materials informatics: Applications of deep learning in materials science
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep materials informatics: Applications of deep learning in materials science
Authors
Keywords
-
Journal
MRS Communications
Volume -, Issue -, Pages 1-14
Publisher
Cambridge University Press (CUP)
Online
2019-06-13
DOI
10.1557/mrc.2019.73
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
- (2019) Brian L. DeCost et al. MICROSCOPY AND MICROANALYSIS
- Material structure-property linkages using three-dimensional convolutional neural networks
- (2018) Ahmet Cecen et al. ACTA MATERIALIA
- Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
- (2018) Zijiang Yang et al. COMPUTATIONAL MATERIALS SCIENCE
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- An online tool for predicting fatigue strength of steel alloys based on ensemble data mining
- (2018) Ankit Agrawal et al. INTERNATIONAL JOURNAL OF FATIGUE
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Crystal Structure Prediction via Deep Learning
- (2018) Kevin Ryan et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- The mythos of model interpretability
- (2018) Zachary C. Lipton COMMUNICATIONS OF THE ACM
- Deep neural networks for accurate predictions of crystal stability
- (2018) Weike Ye et al. Nature Communications
- A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions
- (2018) Xiaolin Li et al. Scientific Reports
- Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks
- (2018) Dipendra Jha et al. MICROSCOPY AND MICROANALYSIS
- Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
- (2018) Zijiang Yang et al. ACTA MATERIALIA
- Machine learning in materials informatics: recent applications and prospects
- (2017) Rampi Ramprasad et al. npj Computational Materials
- Stochastic microstructure characterization and reconstruction via supervised learning
- (2016) Ramin Bostanabad et al. ACTA MATERIALIA
- Social big data: Recent achievements and new challenges
- (2016) Gema Bello-Orgaz et al. Information Fusion
- SILVERBACK+: scalable association mining via fast list intersection for columnar social data
- (2016) Yusheng Xie et al. KNOWLEDGE AND INFORMATION SYSTEMS
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system
- (2015) Akash Gupta et al. ACTA MATERIALIA
- Materials Informatics: The Materials “Gene” and Big Data
- (2015) Krishna Rajan Annual Review of Materials Research
- Crystal structure representations for machine learning models of formation energies
- (2015) Felix Faber et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- A Dictionary Approach to Electron Backscatter Diffraction Indexing
- (2015) Yu H. Chen et al. MICROSCOPY AND MICROANALYSIS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds
- (2015) Romain Gautier et al. Nature Chemistry
- Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials
- (2014) Hongyi Xu et al. COMPUTATIONAL MATERIALS SCIENCE
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power
- (2012) Yuhong Zhang et al. BMC BIOINFORMATICS
- Computational microstructure characterization and reconstruction for stochastic multiscale material design
- (2012) Yu Liu et al. COMPUTER-AIDED DESIGN
- Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
- (2012) Geoffrey Hinton et al. IEEE SIGNAL PROCESSING MAGAZINE
- Microstructure reconstruction using entropic descriptors
- (2010) R. Piasecki PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
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