ArticleChemistry of materials : a publication of the American Chemical Society2022
Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.
Article in Chemistry of materials : a publication of the American Chemical Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Leading the Pack: Next-Generation Batteries for Humanoid Robotics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Text Mining of CVD Synthesis Recipes for 2D Materials.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials.ACS applied materials & interfaces · 2025Article
- Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model.Scientific data · 2025Article
- How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?National science review · 2025Review
- Advancing materials science through next-generation machine learning.Current opinion in solid state & materials science · 2024Article
- ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters.ACS central science · 2024Article
- Extracting accurate materials data from research papers with conversational language models and prompt engineering.Nature communications · 2024Article
- Structured information extraction from scientific text with large language models.Nature communications · 2024Article
- Selective formation of metastable polymorphs in solid-state synthesis.Science advances · 2024Article
- Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation.Chemical science · 2024Article
- Article
- Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid.Journal of the American Chemical Society · 2023Article
- Autonomous and dynamic precursor selection for solid-state materials synthesis.Nature communications · 2023Article
- Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.ACS central science · 2023Article
- Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach.Nature communications · 2023Article
- Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature.Science advances · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis data sets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies (Δ
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