Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis

Energetic Materials Frontiers, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 1, 2023

In this study, machine learning (ML)-assisted regression modeling was conducted to predict the thermal decomposition temperatures and explore factors that correlate with stability of energetic materials (EMs). The performed based on a dataset consisting 885 various compounds using linear nonlinear algorithms. tree-based models established demonstrated acceptable predictive abilities, yielding low mean absolute error (MAE) 31°C. By analyzing through hierarchical classification, study insightfully identified affecting EMs' temperatures, overall accuracy improved targeted modeling. SHapley Additive exPlanations (SHAP) analysis indicated descriptors such as BCUT2D, PEOE_VSA, MolLog_P, TPSA played significant role, demonstrating process is influenced by multiple relating composition, electron distribution, chemical bond properties, substituent type molecules. Additionally, Carbon_contents Oxygen_Balance proposed for characterizing EMs showed strong correlations temperatures. trends their SHAP values most suitable ranges were 0.2–0.35 −65 −55, respectively. Overall, shows potential ML temperature prediction provides insights into characteristics molecular descriptors.

Language: Английский

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Bionic inspired multifunctional modular energetic materials: an exploration of new generation of application-oriented energetic materials

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(16), P. 9427 - 9437

Published: Jan. 1, 2024

Aiming to balance the pertinence and universality of energetic materials, this study proposes a new concept bionic inspired multifunctional modular materials seeks out potential monomers via high-throughput screening strategy.

Language: Английский

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Exploring an accurate machine learning model to quickly estimate stability of diverse energetic materials

iScience, Journal Year: 2024, Volume and Issue: 27(4), P. 109452 - 109452

Published: March 8, 2024

High energy and low sensitivity have been the focus of developing new energetic materials (EMs). However, there has a lack quick accurate method for evaluating stability diverse EMs. Here, we develop machine learning prediction model with high accuracy bond dissociation (BDE) A reliable representative BDE dataset EMs is constructed by collecting 778 experimental compounds quantum mechanics calculation. To sufficiently characterize EMs, hybrid feature representation proposed coupling local target into global structure characteristics. alleviate limitation dataset, pairwise difference regression utilized as data augmentation advantage reducing systematic errors improving diversity. Benefiting from these improvements, XGBoost achieves best R2 0.98 MAE 8.8 kJ mol−1, significantly outperforming competitive models.

Language: Английский

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Probing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High‐Energy Compounds

physica status solidi (RRL) - Rapid Research Letters, Journal Year: 2021, Volume and Issue: 16(3)

Published: June 1, 2021

The relationships between the theoretical values calculated using density functional theory and experimental data derived from differential scanning calorimetry of high‐energy organic compounds are studied. number atoms bonds different types their lengths, minimum eigenfrequencies, atomization energies, ionization potentials, electron affinities, frontier orbital energies. amounts releasing heat (the first peaks higher than 1 kJ g −1 ) corresponding temperatures. Neural networks regression, factor, discriminant, cluster analysis applied to find dependencies data. It is found that amount cannot be predicted in general cases, whereas temperature can with a neural network an accuracy ≈30 °C. Cluster discriminant provides way for classification into three groups. Some these groups require particular rules prediction values.

Language: Английский

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Screening heat-resistant energetic molecules via deep learning and high-throughput computation

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 160218 - 160218

Published: Feb. 1, 2025

Language: Английский

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Applications of Predictive Modeling for Energetic Materials

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 339 - 364

Published: Jan. 1, 2025

Language: Английский

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Application of Machine Learning to the Design of Energetic Materials: Preliminary Experience and Comparison with Alternative Techniques

Propellants Explosives Pyrotechnics, Journal Year: 2022, Volume and Issue: 48(4)

Published: Dec. 10, 2022

Abstract The last few years have seen a steep rise in the use of data‐driven methods different scientific fields historically relying on theoretical or empirical approaches. Chemistry is at forefront this paradigm shift due to longstanding computational tools involved calculation molecular structures and properties. In paper, we showcase examples from literature as well work progress our lab order give brief overview how these can benefit energetic materials community. A deep learning approach compared “traditional” QSPR semi‐empirical approaches for property prediction, specificities inherent are discussed. Deep generative models design new also presented. We conclude by giving view most promising strategies future silico generation satisfying performance/sensitivity trade‐off.

Language: Английский

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EM Database v1.0: A benchmark informatics platform for data-driven discovery of energetic materials

Energetic Materials Frontiers, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 1, 2023

Large-scale data demonstrates great significance for the discovery of novel energetic materials (EMs). However, open-source databases EMs are not readily available. In pursuit high-performance before synthetic attempts in laboratory, theoretically predicted properties and experimental results that can be easily accessed desired. Herein, a benchmark informatics platform EMs, namely EM Database, has been developed purpose storage sharing. Database v1.0 currently contains approximately 100000 unique compounds obtained through quantum chemistry (QC) calculations about 10000 extracted from literature. The QC database were via ground-state density functional using B3LYP/6-31G(d,p) method. These include geometrical conformation, electronic structures, (i.e., crystal density, enthalpy sublimation, molar heat formation, detonation pressure, velocity, heat, volume) models quantitative structure-property relationships. manually collected literature then doubly curated by our project team members. physicochemical, thermal, combustion, detonation, spectra, sensitivity properties. this paper, we also discuss techniques constructing present fundamental features database. is expected to serve as an effective forthcoming research on EMs.

Language: Английский

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Estimating the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy using machine learning

Journal of Applied Physics, Journal Year: 2024, Volume and Issue: 135(13)

Published: April 3, 2024

The lattice thermal conductivity stands as a pivotal thermos-physical parameter of high-entropy alloys; nonetheless, achieving precise predictions the for alloys poses formidable challenge due to their complex composition and structure. In this study, machine learning models were built predict AlCoCrNiFe alloy based on molecular dynamic simulations. Our model shows high accuracy with R2, mean absolute percentage error, root square error test set is 0.91, 0.031, 1.128 W m−1 k−1, respectively. addition, low 2.06 k−1 (Al8Cr30Co19Ni20Fe23) 5.29 (Al0.5Cr28.5Co25Ni25.5Fe20.5) was successfully predicted, which good agreement results from dynamics mechanisms divergence are further explained through phonon density states elastic modulus. established provides powerful tool developing desired properties.

Language: Английский

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Enhancing structural analysis and electromagnetic shielding in carbon foam composites with applications in concrete integrating XGBoost machine learning, carbon nanotubes, and montmorillonite

Synthetic Metals, Journal Year: 2024, Volume and Issue: 307, P. 117656 - 117656

Published: May 31, 2024

Language: Английский

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