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RESEARCH PAPER · Chemistry of Materials · 2020

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

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

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Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific LiteratureOriginalEmerging materials intelligence ecosystems propelled by machine learning441The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science33Opportunities and challenges of text mining in materials research180Synthetic approaches for thin-film halide double perovskites38Observing and Modeling the Sequential Pairwise Reactions that Drive Solid‐State Ceramic Synthesis89An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature0In Situ Mechanistic Studies of Two Divergent Synthesis Routes Forming the Heteroanionic BiOCuSe27Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Panoramic (in beam) studies of materials synthesis1Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature2Prediction of solar cell materials via unsupervised literature learning11Impact of processing conditions on the film formation of lead-free halide double perovskite Cs 2 AgBiBr 637Synthesis and structural studies of superconducting perovskite GdBa2Ca3Cu4O10.5+δ nanosystems102Machine Learning and Natural Language Processing Enable a Data-Oriented Experimental Design Approach for Producing Biochar and Hydrochar from Biomass67A review of the recent progress in battery informatics178Mining Insights on Metal–Organic Framework Synthesis from Scientific Literature Texts72Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water11Text Mining for Energy Materials3Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science163Recent advances and applications of deep learning methods in materials science1161Challenges in Information-Mining the Materials Literature: A Case Study and Perspective21Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature83Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities56Materials Discovery With Machine Learning and Knowledge Discovery40Coordination polymers as precursors of simple and complex oxides0An automatic descriptors recognizer customized for materials science literature36Image-based machine learning for materials science54Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118A high-quality dataset construction method for text mining in materials science20A Text Mining Pipeline for Mining the Quantum Cascade Laser Properties3Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions18Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs26Chemical bath deposition synthesis of Dy2(CO3)3, and their evolution to Dy2O3 transition1Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction41Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys29Modernist materials synthesis: Finding thermodynamic shortcuts with hyperdimensional chemistry19First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface12Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Natural Language Processing Techniques for Advancing Materials Discovery: A Short Review18Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature65ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis536A Novel Anode Material Li2FeGeO4 with Lithium Storage Mechanism Controlled by Both Iron and Germanium Elements8ChemNLP: A Natural Language-Processing-Based Library for Materials Chemistry Text Data46Design principles for NASICON super-ionic conductors147Combining text mining, in situ characterization, and ab initio calculations to rationalize BiFeO3 crystallization pathways8An autonomous laboratory for the accelerated synthesis of inorganic materials1000Development and application of automatic identification methods based on deep learning for oxide scale structures of iron and steel materials7Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO312Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)23Harnessing GPT-3.5 for text parsing in solid-state synthesis – case study of ternary chalcogenides26Insights into the Development of Electrocatalysts Based on Information Extracted from Literatures Using Natural Language Processing1Semantic knowledge graph as a companion for catalyst recommendation6Structured information extraction from scientific text with large language models734Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures22Navigating phase diagram complexity to guide robotic inorganic materials synthesis82Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design9Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles8A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16Concluding remarks: Faraday Discussion on data-driven discovery in the chemical sciences1Data-driven analysis of text-mined seed-mediated syntheses of gold nanoparticles12A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions7Deep reinforcement learning for inverse inorganic materials design59Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data8Exploring the development of electrocatalysts through an automated comprehensive literature review using NLP0Applications of natural language processing and large language models in materials discovery132Advancements of Large Language Models for Enhancing Carbon Capture Technologies: A Comprehensive Review1A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization5Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature0A knowledge acquisition automatizing framework from literature exemplified by Na+ activation energy prediction of NASICON solid-state electrolyte13Accelerated data-driven materials science with the Materials Project300Text Mining for Process–Structure–Properties Relationships in Metals1Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction5MatSciES: Automated Knowledge Extraction and Summarization from Materials Science Literature with Large Language Models2Praseodymium Decorated Pyrimidine COFs for Visible Light Photocatalysis and Optical Applications2Large Language Models (LLMs) for Materials Design14Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model6MaterialBrain: High-Performance Material Synthesis Extraction via Human–AI-Curated Few-Shot Large Language Models2Development of an AI Tool for Systematic Review of Scientific Articles Containing IR-Spectra of Boron Nitride Nano- and Microparticles0From literature to lab: LLM-powered catalyst synthesis protocol generation0Informatics for thermodynamics: Stability to synthesizability0Quantitative analysis of operating conditions and performance trends in CO2 capture from large-scale literature0Protonation-state engineering enables low-temperature and phase-selective synthesis of lanthanide tellurites and high-entropy derivatives0Artificial Intelligence for Materials Science: Transforming Research Paradigms6AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2

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