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RESEARCH PAPER · Nature Communications · 2019

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

Dipendra Jha, Kamal Kumar Choudhary, Francesca Tavazza, Wei‐keng Liao, Alok Choudhary, Carelyn E. Campbell, Ankit Agrawal

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Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learningOriginalCost-effective materials discovery: Bayesian optimization across multiple information sources38Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms2Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique89Randomized resonant metamaterials for single-sensor identification of elastic vibrations75Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations34Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks37Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods114Time to kick-start text mining for biomaterials36Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120A critical examination of compound stability predictions from machine-learned formation energies268Modeling the dielectric constants of crystals using machine learning69Electronic Structure Modeling of Metal–Organic Frameworks329Machine learning formation enthalpies of intermetallics22Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing164Leverage electron properties to predict phonon properties via transfer learning for semiconductors45Smart metasurface shaft for vibration source identification with a single sensor27Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design541A critical examination of compound stability predictions from machine-learned formation energies194Predicting materials properties without crystal structure: deep representation learning from stoichiometry400Residual Neural Network Architectures to Improve Prediction Accuracy of Properties of Materials4A data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors28Active learning and element-embedding approach in neural networks for infinite-layer versus perovskite oxides13Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition1Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries22Modeling the dielectric constants of crystals using machine learning61Monitoring the role of site chemistry on the formation energy of perovskites via deep learning analysis of Hirshfeld surfaces10Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design62Trends in computational molecular catalyst design27Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations.0Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning130Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 st century55Miniaturization of optical spectrometers950Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning50Transfer learning for materials informatics using crystal graph convolutional neural network96Enabling deeper learning on big data for materials informatics applications67Machine Learning‐Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis34Fast Correction of Errors in the DFT‐Calculated Energies of Gaseous Nitrogen‐Containing Species49A multi-objective optimizer-based model for predicting composite material properties26Transfer learning for solvation free energies: From quantum chemistry to experiments170Deep learning for ultra-fast and high precision screening of energy materials55Efficient few-shot machine learning for classification of EBSD patterns45Big data and machine learning for materials science180Can Artificial Intelligence and Machine Learning Be Used to Accelerate Sustainable Chemistry and Engineering?56Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints31Materials Representation and Transfer Learning for Multi-Property Prediction4Materials Representation and Transfer Learning for Multi-Property Prediction1Materials representation and transfer learning for multi-property prediction71An attention-driven long short-term memory network for high throughput virtual screening of organic photovoltaic candidate molecules15Paradigm Shift: The Promise of Deep Learning in Molecular Systems Engineering and Design12Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery0Computationally Enabled Sensors2A method for predicting mechanical properties of composite microstructure with reduced dataset based on transfer learning60Matrix Manipulation of Directly‐Synthesized PbS Quantum Dot Inks Enabled by Coordination Engineering53Accelerating materials discovery with Bayesian optimization and graph deep learning142Innovative Materials Science via Machine Learning194Symmetry-aware recursive image similarity exploration for materials microscopy8AtomSets as a hierarchical transfer learning framework for small and large materials datasets76Machine learning for multiscale modeling in computational molecular design25Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data198DeepTMC: A deep learning platform to targeted design doped transition metal compounds16Structural, electronic, elastic, phonon and thermoelectric properties of Heusler-structured intermetallic HfCu2In: Using density functional theory9Molecular representations for machine learning applications in chemistry93Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives136A small-data-driven model for predicting adsorption properties in polymeric thin films1Application of transfer learning to predict diffusion properties in metal–organic frameworks18Contrastive representation learning of inorganic materials to overcome lack of training datasets7High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network9Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study0The Illusion of Generalization: A Critique of Random Train-Test Splitting in Small-Crystal Property Prediction0光谱域编码的压缩光谱测量技术0Mapping Oxidation and Wafer Cleaning to Device Characteristics Using Physics-Assisted Machine Learning10Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives128An Easy, Simple, and Accessible Web-based Machine Learning Platform, SimPL-ML2MgO/CaO Nanocomposite Facilitates Economical Production of d -Fructose and d -Allulose Using Glucose and Its Response Prediction Using a DNN Model23Deep transfer learning correlation study of electronic and spin properties in buckled III–V monolayers3Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete24Predicting Diffusion Coefficients of Binary and Ternary Supercritical Water Mixtures via Machine and Transfer Learning with Deep Neural Network26Recent advances in the application of machine-learning algorithms to predict adsorption energies32Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides6Deep learning approach for chemistry and processing history prediction from materials microstructure22Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks36CrysXPP: An explainable property predictor for crystalline materials24Revealing geometrically necessary dislocation density from electron backscatter patterns via multi-modal deep learning5Text Mining for Energy Materials3Machine learning predicted magnetic entropy change using chemical descriptors across a large compositional landscape24Deep dive into machine learning density functional theory for materials science and chemistry111Recent advances and applications of deep learning methods in materials science1161Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures11Design and prediction of metal organic framework-based mixed matrix membranes for CO2 capture via machine learning103Data‐Driven Materials Innovation and Applications187Big Data Seismology94Transfer learning using attentions across atomic systems with graph neural networks (TAAG)34Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset35Predicting stress–strain curves using transfer learning: Knowledge transfer across polymer composites87Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications4Effect of reducible and irreducible search space representations on adaptive design efficiency: a case study on maximizing packing fraction for solid rocket fuel propellant simulations2High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO33High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network60Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data17Machine learning modeling for the prediction of materials energy9Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties81On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets78Reflections on one million compounds in the open quantum materials database (OQMD)49A Transfer Learning‐Based Method for Facilitating the Prediction of Unsteady Crystal Growth11Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs56Moving closer to experimental level materials property prediction using AI35Next-generation energy systems for sustainable smart cities: Roles of transfer learning118Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Artificial Intelligence‐Based Material Discovery for Clean Energy Future47Nonlinearity Encoding for Extrapolation of Neural Networks4Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning14Computation-accelerated discovery of the K2NiF4-type oxyhydrides combing density functional theory and machine learning approach1Battery Materials Discovery and Smart Grid Management using Machine Learning8Predicting Experimental Heats of Formation via Deep Learning with Limited Experimental Data7The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization6Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning43Leveraging low-fidelity data to improve machine learning of sparse high-fidelity thermal conductivity data via transfer learning21Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity7Developing a generic relation for predicting sediment pick-up rate using symbolic soft computing techniques2A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures44Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys9Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback60Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks64Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework73Deep Learning of Microstructures0EXPANSE: A Continual and Progressive Learning System for Deep Transfer Learning6Machine learning for membrane design and discovery144Conditional Graph Regression for Complex Chemical Systems with Heterogeneous Substructures0Artificial Intelligence and Advanced Materials118A computational method for the load spectra of large-scale structures with a data-driven learning algorithm12A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Machine Learning Applications in Chemical Kinetics and Thermochemistry9Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network22Operationalizing "Material Similarity" for Domain Adaptation: A Definitional Boundary for Composition-Transfer Learning0Redefining “Out-of-Distribution” for Crystalline Materials Graphs: A Boundary for Domain Shift Detection0Synthetic data enable experiments in atomistic machine learning16Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films14Transfer learning on large datasets for the accurate prediction of material properties36Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces12Real-time plume tracking using transfer learning approach34A rapid and effective method for alloy materials design via sample data transfer machine learning101Machine learning in gas separation membrane developing: Ready for prime time72A Review of Deep Transfer Learning and Recent Advancements680Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning17Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering65Machine-Learning-Based Composition Analysis of the Stability of V–Cr–Ti Alloys4Representations of Materials for Machine Learning133

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