Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
Authors
Keywords
-
Journal
Nature Communications
Volume 10, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2019-11-22
DOI
10.1038/s41467-019-13297-w
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep materials informatics: Applications of deep learning in materials science
- (2019) Ankit Agrawal et al. MRS Communications
- Matminer: An open source toolkit for materials data mining
- (2018) Logan Ward et al. COMPUTATIONAL MATERIALS SCIENCE
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- MoleculeNet: a benchmark for molecular machine learning
- (2018) Zhenqin Wu et al. Chemical Science
- Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
- (2018) Kamal Choudhary et al. Scientific Data
- Machine learning modeling of superconducting critical temperature
- (2018) Valentin Stanev et al. npj Computational Materials
- A strategy to apply machine learning to small datasets in materials science
- (2018) Ying Zhang et al. npj Computational Materials
- Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds
- (2018) Atsuto Seko et al. PHYSICAL REVIEW MATERIALS
- Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
- (2018) Kamal Choudhary et al. PHYSICAL REVIEW MATERIALS
- Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks
- (2018) Dipendra Jha et al. MICROSCOPY AND MICROANALYSIS
- ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
- (2018) Dipendra Jha et al. Scientific Reports
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Universal fragment descriptors for predicting properties of inorganic crystals
- (2017) Olexandr Isayev et al. Nature Communications
- Experimental formation enthalpies for intermetallic phases and other inorganic compounds
- (2017) George Kim et al. Scientific Data
- Machine learning in materials informatics: recent applications and prospects
- (2017) Rampi Ramprasad et al. npj Computational Materials
- Prediction of thermal boundary resistance by the machine learning method
- (2017) Tianzhuo Zhan et al. Scientific Reports
- High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory
- (2017) Kamal Choudhary et al. Scientific Reports
- From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown
- (2016) Chiho Kim et al. CHEMISTRY OF MATERIALS
- High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
- (2016) Anton O. Oliynyk et al. CHEMISTRY OF MATERIALS
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
- (2016) Hoo-Chang Shin et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Machine-learning-assisted materials discovery using failed experiments
- (2016) Paul Raccuglia et al. NATURE
- Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals
- (2016) Felix A. Faber et al. PHYSICAL REVIEW LETTERS
- Accelerated search for materials with targeted properties by adaptive design
- (2016) Dezhen Xue et al. Nature Communications
- A general-purpose machine learning framework for predicting properties of inorganic materials
- (2016) Logan Ward et al. npj Computational Materials
- A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
- (2016) Maarten de Jong et al. Scientific Reports
- Deep learning
- (2015) Yann LeCun et al. NATURE
- The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
- (2015) Scott Kirklin et al. npj Computational Materials
- Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture
- (2014) Michael Fernandez et al. Journal of Physical Chemistry Letters
- Combinatorial screening for new materials in unconstrained composition space with machine learning
- (2014) B. Meredig et al. PHYSICAL REVIEW B
- On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets
- (2014) Aaron Gilad Kusne et al. Scientific Reports
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- (2013) James E. Saal et al. JOM
- Machine learning of molecular electronic properties in chemical compound space
- (2013) Grégoire Montavon et al. NEW JOURNAL OF PHYSICS
- Accelerating materials property predictions using machine learning
- (2013) Ghanshyam Pilania et al. Scientific Reports
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
- (2012) Stefano Curtarolo et al. COMPUTATIONAL MATERIALS SCIENCE
- Optimizing transition states via kernel-based machine learning
- (2012) Zachary D. Pozun et al. JOURNAL OF CHEMICAL PHYSICS
- Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods
- (2012) Eric W. Bucholz et al. TRIBOLOGY LETTERS
- A high-throughput infrastructure for density functional theory calculations
- (2011) Anubhav Jain et al. COMPUTATIONAL MATERIALS SCIENCE
- Formation enthalpies by mixing GGA and GGA+Ucalculations
- (2011) Anubhav Jain et al. PHYSICAL REVIEW B
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now