ArticleChemical science2024
Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites.Micromachines · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-
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Registered trials
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