ArticleScience advances2021
Machine learning-accelerated design and synthesis of polyelemental heterostructures.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Cross-material catalyst discovery via deep learning.Nature materials · 2026Article
- Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi-Variable Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advancing the Landscape of RNAi Nanotherapeutics for Ischemic Heart Disease.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Image processing pipeline for AI-driven nanoparticle megalibrary characterization.Scientific reports · 2026Article
- Hierarchical Embedded Sphere Model: An Interpretable ML-Guided Multiscale Descriptor Engineering Decodes OER Activity on TM@MOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Polyelemental Nanoparticle Synthesis Enabled by Noncontact Metallothermic Reactions.Journal of the American Chemical Society · 2025Article
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Accelerating the Pace of Oxygen Evolution Reaction Catalyst Discovery through Megalibraries.Journal of the American Chemical Society · 2025Article
- Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Traversing the Periodic Table through Phase-Separating Nanoreactors.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Tunable and scalable production of nanostar particle platforms for diverse applications using an AI-integrated automated synthesis system.Journal of materials science · 2025Article
- Creating chromaticity palettes and identifying white light emitters through nanocrystal megalibraries.Science advances · 2025Article
- Multivariate Bayesian Optimization of CoO Nanoparticles for COJournal of the American Chemical Society · 2024Article
- On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set.ACS omega · 2024Article
- Accurate prediction of the optical properties of nanoalloys with both plasmonic and magnetic elements.Nature communications · 2024Article
- ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs.ACS central science · 2023Article
- Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis.Inorganic chemistry · 2023Article
- Rethinking the pathway to sustainable fire retardants.Exploration (Beijing, China) · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning–driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.
Identifiers
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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.