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RESEARCH PAPER · npj Computational Materials · 2019

Semi-supervised machine-learning classification of materials synthesis procedures

Haoyan Huo, Ziqin Rong, Olga Kononova, Wenhao Q. Sun, Tiago Botari, Tanjin He, Vahe Tshitoyan, Gerbrand Ceder

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Semi-supervised machine-learning classification of materials synthesis proceduresOriginalText-mined dataset of inorganic materials synthesis recipes259Battery 2030+ Roadmap46Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks142Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures113Artificial Intelligence to Power the Future of Materials Science and Engineering154Designing bioinspired brick-and-mortar composites using machine learning and statistical learning43Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Automated extraction of chemical synthesis actions from experimental procedures202Machine learning approaches for the prediction of materials properties255Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films18Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature85Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles58Data-driven materials research enabled by natural language processing and information extraction302Can we predict materials that can be synthesised?73Analyzing Research Trends in Inorganic Materials Literature Using NLP8Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection26The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science33Topic modeling and its applications in materials science and engineering24Toward autonomous design and synthesis of novel inorganic materials159Accurate machine learning in materials science facilitated by using diverse data sources53Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 st century55Opportunities and challenges of text mining in materials research180MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature14CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW15Classification of Arabic Tweets: A Review35Machine learning and symbolic regression investigation on stability of MXene materials83Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate1Catalyze Materials Science with Machine Learning67Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature0Machine Learning Classifiers for Surface Crack Detection in Fracture Experiments63Advanced machine learning decision policies for diameter control of carbon nanotubes35Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Predicting synthesizability of crystalline materials via deep learning43Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature2Inverse Design of Nanoparticles Using Multi‐Target Machine Learning41Algorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space0Extracting and Analyzing Inorganic Material Synthesis Procedures in the Literature10New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research53Prediction without Transferability: Domain Shift in Cross-Material AI Inference0ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols28Machine Learning and Natural Language Processing Enable a Data-Oriented Experimental Design Approach for Producing Biochar and Hydrochar from Biomass67A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+235Automatic metadata information extraction from scientific 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  2. Battery 2030+ Roadmap

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  3. Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

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  12. Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles

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  15. Analyzing Research Trends in Inorganic Materials Literature Using NLP

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