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RESEARCH PAPER · Scientific Reports · 2018

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

Dipendra Jha, Logan T. Ward, Arindam Paul, Wei‐keng Liao, Alok Choudhary, Chris M. Wolverton, Ankit Agrawal

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ElemNet: Deep Learning the Chemistry of Materials From Only Elemental CompositionOriginalMicrostructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation47Computational screening of electrolyte materials: status quo and open problems33Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules70Deep materials informatics: Applications of deep learning in materials science294IRNet47Atom table convolutional neural networks for an accurate prediction of compounds properties98Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation337Machine learning in materials science1058Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees37A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems44A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes65Digital Twin: Values, Challenges and Enablers73Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions476Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning339Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns181Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning75Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Computational Prediction of Critical Temperatures of Superconductors Based on Convolutional Gradient Boosting Decision Trees32Deep learning enabled inorganic material generator52Digital Twin: Values, Challenges and Enablers From a Modeling Perspective1912High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78A Critical Review of Machine Learning of Energy Materials606Toward Accelerated Thermoelectric Materials and Process Discovery102Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning34Machine Learning-Based Prediction of Crystal Systems and Space Groups from Inorganic Materials Compositions70Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Theory-training deep neural networks for an alloy solidification benchmark problem39Learning physical properties of liquid crystals with deep convolutional neural networks73Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery281Deep learning analysis on microscopic imaging in materials science195Covalency competition dominates the water oxidation structure–activity relationship on spinel oxides611Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials295A critical examination of compound stability predictions from machine-learned formation energies268Electronic Structure Modeling of Metal–Organic Frameworks329Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning40Is Domain Knowledge Necessary for Machine Learning Materials Properties?106Artificial intelligence for photonics and photonic materials63Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors47Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural 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