ArticleiScience2024
Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning prediction of thermal sensitivity for energetic materials based on multi-source feature fusion.Journal of molecular modeling · 2026Article
- Electronic, mechanical, vibrational, and thermodynamic properties of 5,5'-bis(1,4-dinitro-1H-pyrazol-3-yl)-2,2'-bi(1,3,4-oxadiazole) based on first‑principles calculations.Journal of molecular modeling · 2026Article
- Computational Design and Theoretical Evaluation of Novel Heterocyclic Energetic Materials Based on 5-H-Imidazo-[4,5-c]-Pyridazine and 1,2,4-Triazolo-[4,3-a]-Pyrazine Scaffolds.Journal of computational chemistry · 2026Article
- Fundamentals to Prospects: Synthetic Tuning of Covalent Organic Frameworks for Solid-State Hydrogen Storage.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- BEmXRD-Nets framework for novel machine learning models to predict crystal energy with diversity structures.Scientific reports · 2026Article
- Unraveling the impact sensitivity mechanism of energetic materials from vibrational and electronic energy transfer.iScience · 2025Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has been a lack of a quick and accurate method for evaluating the stability of diverse EMs. Here, we develop a machine learning prediction model with high accuracy for bond dissociation energy (BDE) of EMs. A reliable and representative BDE dataset of EMs is constructed by collecting 778 experimental energetic compounds and quantum mechanics calculation. To sufficiently characterize the BDE of EMs, a hybrid feature representation is proposed by coupling the local target bond into the global structure characteristics. To alleviate the limitation of the low dataset, pairwise difference regression is utilized as a data augmentation with the advantage of reducing systematic errors and improving diversity. Benefiting from these improvements, the XGBoost model achieves the best prediction accuracy with R
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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.