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RESEARCH PAPER · Journal of Applied Physics · 2018

Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides

Steven R. Young, Artem Maksov, Maxim A Ziatdinov, Ye Cao, Matthew J. Burch, Janakiraman Balachandran, Linglong Li, Suhas Somnath, Robert M. Patton, Sergei V. Kalinin, Rama K. Vasudevan

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Data mining for better material synthesis: The case of pulsed laser deposition of complex oxidesOriginalMaterials‐Informatics‐Assisted High‐Yield Synthesis of 2D Nanomaterials through Exfoliation32Semi-supervised machine-learning classification of materials synthesis procedures150Active Learning Yields Better Training Data for Scientific Named Entity Recognition16Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks142The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data29Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge64Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Data-driven materials research enabled by natural language processing and information extraction302Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films38Analyzing Research Trends in Inorganic Materials Literature Using NLP8The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science33Opportunities and challenges of text mining in materials research180Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration65MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature14Machine learning analysis of microwave dielectric properties for seven structure types: The role of the processing and composition19Machine learning for high-throughput experimental exploration of metal halide perovskites117Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries72Multi-dimensional characteristic construction methods of computational materials under big data environment1Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science163Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions79The value of negative results in data-driven catalysis research72Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning13Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature7Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra16MXenes-mining: a decade of discovery19Towards a science exocortex11High-Throughput Extraction of Phase–Property Relationships from Literature Using Natural Language Processing and Large Language Models9Novelty detection in the design of synthesis of garnet-structured solid electrolytes6Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics12Epitaxial oxide ionotronics: Interfaces and oxygen vacancies9Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data8How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Input Consistency Regularization of Experimental Data for Modeling Functional Characteristics of A2M3O12${\rm A}_{2} {\rm M}_{3} {\rm O}_{12}$ Family of Compounds0Data-driven high-throughput search for the accelerated discovery of rare-earth-free permanent magnets1POLARIS: perovskite optimization using LLM-assisted refinement and intelligent screening2Transformative power of AI-driven knowledge extraction in materials science: From fragmented pipelines to unified understanding0

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  2. Semi-supervised machine-learning classification of materials synthesis procedures

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  3. Active Learning Yields Better Training Data for Scientific Named Entity Recognition

    International Conference on e-Science · 2019-09-01

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

    Journal of Chemical Information and Modeling · 2020-01-07

    Edward Kim, Zach Jensen, Alexander van Grootel, Kejun Huang, Matthew Staib, Sheshera Mysore, Haw-Shiuan Chang, Emma Strubell, Andrew McCallum, Stefanie Jegelka, Elsa Olivetti

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  5. The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research

    Computational Materials Science · 2020-01-28

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  6. Materials Informatics Screening of Li‐Rich Layered Oxide Cathode Materials with Enhanced Characteristics Using Synthesis Data

    Batteries & Supercaps · 2020-02-03

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  7. Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

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  8. Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

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  9. Data-driven materials research enabled by natural language processing and information extraction

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  10. Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films

    Journal of Applied Physics · 2020-12-17

    Anna Costine, Paige Delsa, Tianxi Li, Petra Reinke, Prasanna V. Balachandran

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  11. Analyzing Research Trends in Inorganic Materials Literature Using NLP

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  12. The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science

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    Nicholas Walker, Amalie Trewartha, Haoyan Huo, Sang-Hoon Lee, Kevin Cruse, John M. Dagdelen, Alexander Dunn, Kristin Aslaug Persson, Gerbrand Ceder, Anubhav Jain

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  13. Opportunities and challenges of text mining in materials research

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    Olga Kononova, Tanjin He, Haoyan Huo, Amalie Trewartha, Elsa Olivetti, Gerbrand Ceder

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  14. Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

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    Seungbum Hong, Chi Hao Liow, Jong Min Yuk, Hye Ryung Byon, Yongsoo Yang, EunAe Cho, Jiwon Yeom, Gun Park, Hyeonmuk Kang, Seung-Gu Kim, Yoonsu Shim, Moony Na, Chaehwa Jeong, Gyuseong Hwang, Hongjun Kim, Hoon Kim, Seongmun Eom, Seongwoo Cho, Hosun Jun, Yongju Lee, Arthur Baucour, Kihoon Bang, Myungjoon Kim, Seokjung Yun, Jeongjae Ryu, Youngjoon Han, Albina Jetybayeva, Pyuck‐Pa Choi, Joshua Agar, Sergei V. Kalinin, Peter W. Voorhees, P. B. Littlewood, Hyuck Mo Lee

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  15. MatScIE: An automated tool for the generation of databases of methods and parameters used in the computational materials science literature

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  16. Machine learning analysis of microwave dielectric properties for seven structure types: The role of the processing and composition

    Journal of Physics and Chemistry of Solids · 2021-05-24

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  17. Machine learning for high-throughput experimental exploration of metal halide perovskites

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  18. Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

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  19. Multi-dimensional characteristic construction methods of computational materials under big data environment

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  20. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science

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  21. Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions

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  22. The value of negative results in data-driven catalysis research

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  23. Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

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  24. Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning

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