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RESEARCH PAPER · Scientific Data · 2017

Machine-learned and codified synthesis parameters of oxide materials

Edward Kim, Kevin J. Huang, Alex Tomala, Sara Matthews, Emma Strubell, Adam M. Saunders, Andrew McCallum, Elsa Olivetti

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Machine-learned and codified synthesis parameters of oxide materialsOriginalMaterials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning472Virtual screening of inorganic materials synthesis parameters with deep learning189The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics481A Relation Aware Search Engine for Materials Science11An open experimental database for exploring inorganic materials202Machine learning in catalysis479Machine learning for heterogeneous catalyst design and discovery444A strategy to apply machine learning to small datasets in materials science776Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons200Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction135Machine learning for predicting occurrence of interphase precipitation in HSLA steels28Strategies for accelerating the adoption of materials informatics39Empirical modeling of dopability in diamond-like semiconductors46Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design832Machine learning based prediction of metal hydrides for hydrogen storage, part I: Prediction of hydrogen weight percent135Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora16A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction282A Bayesian framework for materials knowledge systems29The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures88Unsupervised word embeddings capture latent knowledge from materials science literature1115Semi-supervised machine-learning classification of materials synthesis procedures150Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature307Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science12A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map17Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Graph similarity drives zeolite diffusionless transformations and intergrowth79Text-mined dataset of inorganic materials synthesis recipes259Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding13ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images58Comparative dataset of experimental and computational attributes of UV/vis absorption spectra100Multimodal Characterization of Materials and Decontamination Processes for Chemical Warfare Protection29Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks142Toward “On‐Demand” Materials Synthesis and Scientific Discovery through Intelligent Robots76Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature14A Design-to-Device Pipeline for Data-Driven Materials Discovery114Perovskite Oxides Prepared by Hydrothermal and Solvothermal Synthesis: A Review of Crystallisation, Chemistry, and Compositions152Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Opportunities and Challenges for Machine Learning in Materials Science523Recent advances in knowledge discovery for heterogeneous catalysis using machine learning130A Prediction of Future States: AI-powered Chemical Innovation 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