ReviewNational science review2023
MAGUS: machine learning and graph theory assisted universal structure searcher.
Review in National science review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
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Who cites it
14 citing papers in PubMed.
- GMUPC: a new package for global-minimum search of large uranyl peroxide clusters with machine learning potentials and DFT calculations.Journal of molecular modeling · 2026Article
- Surface Terminations of LaAlO3 Perovskite Nanoparticles as Viewed by Solid-State Nuclear Magnetic Resonance.Journal of the American Chemical Society · 2026Article
- Prediction of thermally driven quasi-1D superionic states in carbon hydride under giant planetary conditions.Nature communications · 2026Article
- Data-Driven Crystal Structure Prediction for Ternary Metal Chalcogenides.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- Deep learning generative model for conditional crystal structure prediction of sodium amide.npj computational materials · 2026Article
- CrystalFlow: a flow-based generative model for crystalline materials.Nature communications · 2025Article
- Low-energy tetrahedral networks for carbon and silicon from (2+1)-regular bipartite-like graphs.IUCrJ · 2025Article
- Machine Learning simulations reveal oxygen's phase diagram and thermal properties at conditions relevant to white dwarfs.Nature communications · 2025Article
- Global universal scaling and ultrasmall parameterization in machine-learning interatomic potentials with superlinearity.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Phase transitions and dimensional cross-over in layered confined solids.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine Learning Model for the Prediction of HubbardJournal of chemical theory and computation · 2024Article
- General-purpose machine-learned potential for 16 elemental metals and their alloys.Nature communications · 2024Article
- Superconducting ternary hydrides: progress and challenges.National science review · 2024Review
- DeePMD-kit v2: A software package for deep potential models.The Journal of chemical physics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially the complexity of conformational space and the cost of local optimizations for big systems. Here, we introduce a crystal structure prediction method, MAGUS, based on the evolutionary algorithm, which addresses the above challenges with machine learning and graph theory. Techniques used in the program are summarized in detail and benchmark tests are provided. With intensive tests, we demonstrate that on-the-fly machine-learning potentials can be used to significantly reduce the number of expensive first-principles calculations, and the crystal decomposition based on graph theory can efficiently decrease the required configurations in order to find the target structures. We also summarized the representative applications of this method on several research topics, including unexpected compounds in the interior of planets and their exotic states at high pressure and high temperature (superionic, plastic, partially diffusive state, etc.); new functional materials (superhard, high-energy-density, superconducting, photoelectric materials), etc. These successful applications demonstrated that MAGUS code can help to accelerate the discovery of interesting materials and phenomena, as well as the significant value of crystal structure predictions in general.
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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.