ArticleScientific data2022
Novel inorganic crystal structures predicted using autonomous simulation agents.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Accelerated data-driven materials science with the Materials Project.Nature materials · 2025Review
- Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models.Angewandte Chemie (International ed. in English) · 2025Article
- Wide-ranging predictions of new stable compounds powered by recommendation engines.Science advances · 2025Article
- Crystal structure generation with autoregressive large language modeling.Nature communications · 2024Article
- An electrochemical series for materials.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.Digital discovery · 2024Article
- How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.Chemical science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report a dataset of 96640 crystal structures discovered and computed using our previously published autonomous, density functional theory (DFT) based, active-learning workflow named CAMD (Computational Autonomy for Materials Discovery). Of these, 894 are within 1 meV/atom of the convex hull and 26826 are within 200 meV/atom of the convex hull. The dataset contains DFT-optimized pymatgen crystal structure objects, DFT-computed formation energies and phase stability calculations from the convex hull. It contains a variety of spacegroups and symmetries derived from crystal prototypes derived from known experimental compounds, and was generated from active learning campaigns of various chemical systems. This dataset can be used to benchmark future active-learning or generative efforts for structure prediction, to seed new efforts of experimental crystal structure discovery, or to construct new models of structure-property relationships.
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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.