Your browser doesn't support javascript.
loading
Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.
Yang, Hang; Yang, Zhi-Jiang; Huang, Teng-Xin; Pan, Li; Wei, Xin-Miao; Hu, Yan-Fei; Yuan, Yu-Quan; Wang, Liang-Liang; Ding, Jun-Jie.
Afiliación
  • Yang H; State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
  • Yang ZJ; School of Physics and Electronics Engineering, Sichuan University of Science & Engineering, Zigong 643000, China.
  • Huang TX; State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
  • Pan L; School of Physics and Electronics Engineering, Sichuan University of Science & Engineering, Zigong 643000, China.
  • Wei XM; State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
  • Hu YF; State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
  • Yuan YQ; Department of Applied Physics, Chengdu University of Technology, Chengdu 610059, China.
  • Wang LL; School of Physics and Electronics Engineering, Sichuan University of Science & Engineering, Zigong 643000, China.
  • Ding JJ; State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China.
ACS Omega ; 9(24): 26213-26221, 2024 Jun 18.
Article en En | MEDLINE | ID: mdl-38911735
ABSTRACT
Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https//sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Omega Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Omega Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos