ReviewMolecules (Basel, Switzerland)2022
Prediction and Construction of Energetic Materials Based on Machine Learning Methods.
Review in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Machine learning innovations for reliable gurney energy estimation in energetic materials.iScience · 2026Article
- Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials.Chemistry of materials : a publication of the American Chemical Society · 2026Article
- A Deep Neural Network Potential Study on the Mechanical and Thermophysical Properties of β‑HMX Molecular Crystal.ACS omega · 2026Article
- Bioactive compounds from marine algae in pancreatic cancer therapy: mechanistic insights into fucoidan and phlorotannins: a review.Medical oncology (Northwood, London, England) · 2025Review
- Machine Learning Models for High Explosive Crystal Density and Performance.Chemistry of materials : a publication of the American Chemical Society · 2024Article
- Article
- Transforming cancer detection and treatment with nanoflowers.Medical oncology (Northwood, London, England) · 2024Review
- Article
- Microbial dynamics, chemical profile, and bioactive potential of diverse Egyptian marine environments from archaeological wood to soda lake.Scientific reports · 2024Article
- Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks.The journal of physical chemistry. A · 2024Article
- Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials.Molecules (Basel, Switzerland) · 2023Review
- Property prediction of energetic materials based on directional-aware graph attention network.Science progressArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced materials were discovered through a trial-and-error processes, which required long research and development (R&D) cycles and high costs. In recent years, the machine learning (ML) method has matured into a tool that compliments and aids experimental studies for predicting and designing advanced EMs. This paper reviews the critical process of ML methods to discover and predict EMs, including data preparation, feature extraction, model construction, and model performance evaluation. The main ideas and basic steps of applying ML methods are analyzed and outlined. The state-of-the-art research about ML applications in property prediction and inverse material design of EMs is further summarized. Finally, the existing challenges and the strategies for coping with challenges in the further applications of the ML methods are proposed.
Indexed as
Identifiers
What OpenQuestion holds
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.