Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To sp...Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.展开更多
借助高速摄影观察发现,水稻气力式精量穴播排种器吸种盘上吸孔所吸附的种子会由于吸力不足,在离心力作用下,在到达投种区前从吸孔附近落下,从而产生＂飞种＂现象,进而对排种器排出的每穴种子数量以及成穴性产生影响,降低排种精度。为此...借助高速摄影观察发现,水稻气力式精量穴播排种器吸种盘上吸孔所吸附的种子会由于吸力不足,在离心力作用下,在到达投种区前从吸孔附近落下,从而产生＂飞种＂现象,进而对排种器排出的每穴种子数量以及成穴性产生影响,降低排种精度。为此,设计了一种挡种装置,以含水率为21.1%的培杂泰丰种子为对象,采用多因素试验的方法,研究了不同吸室负压和不同排种盘转速下,安装挡种装置前后对＂飞种＂现象的影响;采用单因素试验的方法,研究了安装挡种装置后不同吸室负压下,不同排种盘转速对排种器吸种精度的影响。结果表明,安装挡种装置后,＂飞种＂出现范围减小,＂飞种＂出现的数量减少,排种器排种精度与成穴性能提高;当转速在25～40 r/min,吸室负压1.6 k Pa时,（1-3）粒/穴概率在93%～97%之间变化。试验结果显示安装挡种装置后能控制＂飞种＂的跌落范围,并使部分＂飞种＂落回充种室内,从而提高排种器排种精度。展开更多
文摘Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.
文摘借助高速摄影观察发现,水稻气力式精量穴播排种器吸种盘上吸孔所吸附的种子会由于吸力不足,在离心力作用下,在到达投种区前从吸孔附近落下,从而产生＂飞种＂现象,进而对排种器排出的每穴种子数量以及成穴性产生影响,降低排种精度。为此,设计了一种挡种装置,以含水率为21.1%的培杂泰丰种子为对象,采用多因素试验的方法,研究了不同吸室负压和不同排种盘转速下,安装挡种装置前后对＂飞种＂现象的影响;采用单因素试验的方法,研究了安装挡种装置后不同吸室负压下,不同排种盘转速对排种器吸种精度的影响。结果表明,安装挡种装置后,＂飞种＂出现范围减小,＂飞种＂出现的数量减少,排种器排种精度与成穴性能提高;当转速在25～40 r/min,吸室负压1.6 k Pa时,（1-3）粒/穴概率在93%～97%之间变化。试验结果显示安装挡种装置后能控制＂飞种＂的跌落范围,并使部分＂飞种＂落回充种室内,从而提高排种器排种精度。
