Chemical Feature-Based Machine Learning Model for Predicting Photophysical Properties of BODIPY Compounds: Density Functional Theory and Quantitative Structure–Property Relationship Modeling DOI Creative Commons
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 14, 2024

Abstract Boron-dipyrromethene (BODIPY) compounds have unique photophysical properties and been applied in fluorescence imaging, sensing, optoelectronics, beyond. In order to design effective BODIPY compounds, it is crucial acquire a comprehensive understanding of the relationships between structures corresponding photoproperties. present study, DFT/TDDFT was optimize studied models obtain their absorpton spectrum. Based upon theoretical computaional results, machine learning-based Quantitative Structure-Property Relationship (ML/QSPR) model employed for predicting maximum absorption wavelength (λ) by combining hand-crafted molecular descriptors (MD) Explainable Machine Learning (EML) techniques. A dataset 131 with experimental properties, used generate diverse set capturing information about size, shape, connectivity other structural features these compounds. Then genetic algorithm (GA) wrapper Multi-Linear Regression (MLR) performed. Fifteen were identified be strongly correlated wavelength. The developed ML/QSPR exhibited good predictive performance, coefficients determination (R2) 0.945 training 0.734 test set, demonstrating robustness reliability. posterior analysis some selected provided insights into that influence compound meanwhile also emphasizes importance branching, specific functional groups. Our work shows plausible learning approaches screen novel enhanced performance spectra.

Language: Английский

π-π2max: Bridging Molecular Characteristics to Crystal Packing in Nitro-Containing Two-Dimensional Energetic Materials DOI Creative Commons
Xiaokai He, Chao Chen, Zhixiang Zhang

et al.

Defence Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Applications of Predictive Modeling for Energetic Materials DOI
Nasser Sheibani

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

Published: Jan. 1, 2025

Language: Английский

Citations

0
Predictive Modeling for Energetic Materials DOI
Didier Mathieu

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 265 - 310

Published: Jan. 1, 2025

Language: Английский

Citations

0
Development of linear structure property relationship for energetic materials using machine learning DOI

David A. Newsome,

Ghanshyam L. Vaghjiani, Steven D. Chambreau

et al.

Journal of Molecular Liquids, Journal Year: 2025, Volume and Issue: unknown, P. 127480 - 127480

Published: March 1, 2025

Language: Английский

Citations

0
Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds DOI
Haitao Liu, Peng Chen, Chaoyang Zhang

et al.

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: 128(41), P. 9045 - 9054

Published: Oct. 8, 2024

Thermal resistance of energetic materials is critical due to its impact on safety and sustainability. However, developing predictive models remains challenging because data scarcity limited insights into quantitative structure–property relationships. In this work, a deep learning framework, named EM-thermo, was proposed address these challenges. A set comprising 5029 CHNO compounds, including 976 constructed facilitate study. EM-thermo employs molecular graphs direct message-passing neural networks capture structural features predict thermal resistance. Using transfer learning, the model achieves an accuracy approximately 97% for predicting thermal-resistance property (decomposition temperatures above 573.15 K) in compounds. The involvement descriptors improved prediction. These findings suggest that effective correlating from atom covalent bond level, offering promising tool advancing design discovery field

Language: Английский

Citations

1
Application of machine learning in developing a quantitative structure–property relationship model for predicting the thermal decomposition temperature of nitrogen-rich energetic ionic salts DOI Creative Commons
Yunling Zhang, Fan Liang, Chao Su

et al.

RSC Advances, Journal Year: 2024, Volume and Issue: 14(51), P. 37737 - 37751

Published: Jan. 1, 2024

A reliable QSPR model of thermal decomposition temperature ( T d ) was built and developed using support vector machine (SVM) learning technology to predict the property newly designed nitrogen-rich energetic ionic salts.

Language: Английский

Citations

1
Theoretical advances in understanding and enhancing the thermostability of energetic materials DOI Creative Commons

Runze Liu,

Jian‐Yong Liu, Panwang Zhou

et al.

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(41), P. 26209 - 26221

Published: Jan. 1, 2024

The quest for thermally stable energetic materials is pivotal in advancing the safety of applications ranging from munitions to aerospace.

Language: Английский

Citations

0
Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling DOI
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou

et al.

Journal of Molecular Modeling, Journal Year: 2024, Volume and Issue: 31(1)

Published: Dec. 12, 2024

Language: Английский

Citations

0
Chemical Feature-Based Machine Learning Model for Predicting Photophysical Properties of BODIPY Compounds: Density Functional Theory and Quantitative Structure–Property Relationship Modeling DOI Creative Commons
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 14, 2024

Abstract Boron-dipyrromethene (BODIPY) compounds have unique photophysical properties and been applied in fluorescence imaging, sensing, optoelectronics, beyond. In order to design effective BODIPY compounds, it is crucial acquire a comprehensive understanding of the relationships between structures corresponding photoproperties. present study, DFT/TDDFT was optimize studied models obtain their absorpton spectrum. Based upon theoretical computaional results, machine learning-based Quantitative Structure-Property Relationship (ML/QSPR) model employed for predicting maximum absorption wavelength (λ) by combining hand-crafted molecular descriptors (MD) Explainable Machine Learning (EML) techniques. A dataset 131 with experimental properties, used generate diverse set capturing information about size, shape, connectivity other structural features these compounds. Then genetic algorithm (GA) wrapper Multi-Linear Regression (MLR) performed. Fifteen were identified be strongly correlated wavelength. The developed ML/QSPR exhibited good predictive performance, coefficients determination (R2) 0.945 training 0.734 test set, demonstrating robustness reliability. posterior analysis some selected provided insights into that influence compound meanwhile also emphasizes importance branching, specific functional groups. Our work shows plausible learning approaches screen novel enhanced performance spectra.

Language: Английский

Citations

0