The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
文摘[目的]溴虫氟苯双酰胺是新型间二酰胺类杀虫剂,本文通过棉铃虫(Helicoverpa armigera)对溴虫氟苯双酰胺的室内抗性发展情况的探究和田间抗性等位基因频率的调查,全面评估棉铃虫对溴虫氟苯双酰胺的抗性风险,为该药剂的合理使用提供科学依据。[方法]用溴虫氟苯双酰胺对棉铃虫室内品系SCD进行连续多代筛选,根据阈性状分析法计算其抗性现实遗传力(realized heritability of resistance,h^(2));用F_(2)筛选法检测棉铃虫田间种群对溴虫氟苯双酰胺的抗性等位基因频率。[结果]70%左右死亡率的选择压力下连续筛选10代和20代,棉铃虫对溴虫氟苯双酰胺的抗性现实遗传力(h^(2))分别为0.1268和0.0592;在筛选过程中棉铃虫对溴虫氟苯双酰胺的抗性倍数为1.9~4.5,到第20代时抗性倍数为2.8。2022年华北棉区325个棉铃虫田间单雌系F_(2)代幼虫共31200头在溴虫氟苯双酰胺诊断浓度下全部死亡,说明检测的田间单雌系均未携带抗性等位基因,华北棉区棉铃虫田间种群对溴虫氟苯双酰胺的抗性等位基因频率为0.0008(95%CI:0.0000~0.0050)。[结论]棉铃虫室内品系连续多代筛选对溴虫氟苯双酰胺的抗性发展缓慢,F_(2)筛选检测表明田间种群的起始抗性等位基因频率低,因此棉铃虫对溴虫氟苯双酰胺的抗性风险低,可有效用于棉铃虫的防治。
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
