Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

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