ArticleNature communications2024
Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Harnessing Metal-Halide Layered Perovskite Structures for Next-Generation Lighting Sources.Accounts of materials research · 2026Article
- Fingerprinting organic molecules for the inverse design of two-dimensional hybrid perovskites with target energetics.Science advances · 2026Article
- Continuous discovery of novel 2D materials via dual active learning-driven generative models.National science review · 2026Article
- Nanocrystal Synthesis Derived Approach to Silver Bismuth Iodide Layered Double Perovskites with Aliphatic Amines: (CChemistry of materials : a publication of the American Chemical Society · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models.Nature communications · 2025Article
- How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?National science review · 2025Review
- Review
- Interplay of Phonon Directionality and Emission Polarization in Two-Dimensional Layered Metal Halide Perovskites.Accounts of chemical research · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.