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RESEARCH PAPER · Science Advances · 2023

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Tanjin He, Haoyan Huo, Christopher J. Bartel, Zheren Wang, Kevin Cruse, Gerbrand Ceder

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Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literatureOriginalCross-Property Entanglement in Multi-Task Materials Learning Systems0Realizing the cooking recipe of materials synthesis through large language models14Autonomous decision making for solid-state synthesis of inorganic materials1Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials29Predicting Ionic Conductivity in Thin Films of Garnet Electrolytes Using Machine Learning13Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence7In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature7Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials41Autonomous and dynamic precursor selection for solid-state materials synthesis47Accelerating the prediction of stable materials with machine learning104Soft Chemistry of Hard Materials: Low-Temperature Pathways to Bulk and Nanostructured Layered Metal Borides17An autonomous laboratory for the accelerated synthesis of inorganic materials994Artificial Intelligence Aspect Of Transportation Analysis Using Large Scale Systems21Challenges and opportunities for AI in synthetic solid-state inorganic chemistry2Two-Dimensional CN Material Structure Prediction Based on Machine Learning0Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes1Accelerating materials language processing with large language models78Novelty detection in the design of synthesis of garnet-structured solid electrolytes6Creation of a structured solar cell material dataset and performance prediction using large language models28Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries15Computationally Guided Synthesis of Battery Materials11Risk governance and optimization of the intelligent news algorithm recommendation mechanism0Advances in developing cost-effective carbon fibers by coupling multiscale modeling and experiments: A critical review36Large Language Models for Inorganic Synthesis Predictions73A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives61Transforming the synthesis of carbon nanotubes with machine learning models and automation42How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Foundation models for materials discovery – current state and future directions101Automated Discovery of Therapeutic Biomaterial for Renally Impaired Hyperuricemia Patients by Natural Language Processing and Machine Learning0LLM-Driven Synthesis Planning for Quantum Dot Materials Development7Boosting crystal structure prediction via symmetry2Applications of natural language processing and large language models in materials discovery131A Perspective on Foundation Models in Chemistry58Machine learning investigation of the effects of elemental doping on the mechanical properties of Fe-Cr-Ni-Al high-entropy alloys3AI-driven new materials development and data standardization4Large language models for batteries31Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery4Ultrafast synthesis of ternary oxides via a design-test-make-analyze workflow1Artificial intelligence for post-moore field-effect transistors: a review and perspective1Explainable Recommendation Engines to Predict Complex Intermetallics: Synthesis and Characterization of Gd10RuCd3, a Neutron Absorption Material1Identification of Impurity-Minimizing Synthesis Pathways for LAGP via Thermodynamic and Chemical Reaction Network-Based Analysis0Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials8Process optimization for synthesizing α-Al2O3 with tailored morphology and dispersibility by interpretable incremental machine learning3Development of an AI Tool for Systematic Review of Scientific Articles Containing IR-Spectra of Boron Nitride Nano- and Microparticles0Automated machine learning predicting SO₂ and CO₂ adsorption and separation in metal organic frameworks0Informatics for thermodynamics: Stability to synthesizability0Sustaining the growth of the materials informatics ecosystem0Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures3DiffSyn: a generative diffusion approach to materials synthesis planning8Data-driven prediction of solid state reaction pathways in the synthesis of ferrites2A 30-Year Journey of a Synthetic Nanochemist toward Uniform Nanocrystals0Protonation-state engineering enables low-temperature and phase-selective synthesis of lanthanide tellurites and high-entropy derivatives0Artificial Intelligence for Materials Science: Transforming Research Paradigms6A survey of agentic materials science and engineering: where are we and where are we going?0Predicting Inorganic Material Synthesis Pathways Based on Language Models0Deep transfer learning for fast dispatching-rule outcome prediction in industrial-scale FJSP via AdaGate-HGAT0Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2Generative Models in Inorganic Crystals Discovery and Inverse Design0An Investigation into the Kinetic Persistence of TiO2 Polymorphs Using Machine-Learning-Driven Pathfinding in Crystal Configuration Space0A collaborative agent with two lightweight synergistic models for autonomous crystal materials research0Toward automated discovery with generative models multimodal learning and closed loop workflows in inverse materials design0

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