A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the develo...A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.展开更多
目的研究微学习教学法在急危重症综合救治能力培训中的应用效果。方法选取2022年1月—2023年6月于徐州医科大学附属医院东院重症医学科(intensive care unit,ICU)培训的25名学员纳入微学习组,另将徐州医科大学附属医院本部ICU培训的25...目的研究微学习教学法在急危重症综合救治能力培训中的应用效果。方法选取2022年1月—2023年6月于徐州医科大学附属医院东院重症医学科(intensive care unit,ICU)培训的25名学员纳入微学习组,另将徐州医科大学附属医院本部ICU培训的25名学员纳入对照组。对照组采取传统教学法培训,微学习组采取传统教学基础上联合微学习教学法培训,对比2组学员出科理论考试成绩、技能考试成绩以及对教学方法的满意度测评。结果出科考核微学习组学员的理论成绩和技能总成绩均高于对照组[(82.16±6.78)分、(84.32±3.09)分vs.(78.04±6.67)分、(77.84±3.32)分],差异有统计学意义(P<0.05);微学习组教学满意度评分高于对照组[(93.16±3.25)分vs.(88.48±5.00)分],差异有统计学意义(P<0.001)。结论在急危重症综合救治能力培训中应用微学习教学法,学员的理论和技能成绩更高,满意度更高。展开更多
基金support from the Ministry of Education(MOE) Singapore Tier 1 (RG8/20)。
文摘A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.
文摘目的研究微学习教学法在急危重症综合救治能力培训中的应用效果。方法选取2022年1月—2023年6月于徐州医科大学附属医院东院重症医学科(intensive care unit,ICU)培训的25名学员纳入微学习组,另将徐州医科大学附属医院本部ICU培训的25名学员纳入对照组。对照组采取传统教学法培训,微学习组采取传统教学基础上联合微学习教学法培训,对比2组学员出科理论考试成绩、技能考试成绩以及对教学方法的满意度测评。结果出科考核微学习组学员的理论成绩和技能总成绩均高于对照组[(82.16±6.78)分、(84.32±3.09)分vs.(78.04±6.67)分、(77.84±3.32)分],差异有统计学意义(P<0.05);微学习组教学满意度评分高于对照组[(93.16±3.25)分vs.(88.48±5.00)分],差异有统计学意义(P<0.001)。结论在急危重症综合救治能力培训中应用微学习教学法,学员的理论和技能成绩更高,满意度更高。
