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
基于集成学习理论,首次将人工神经网络和极端梯度提升算法进行集成,提出一种全新的算法:HEMNG(hybrid ensemble model based on neural networks and gradient boosting),旨在更准确地预测混凝土抗压强度。采用303组混凝土配合比数据进...基于集成学习理论,首次将人工神经网络和极端梯度提升算法进行集成,提出一种全新的算法:HEMNG(hybrid ensemble model based on neural networks and gradient boosting),旨在更准确地预测混凝土抗压强度。采用303组混凝土配合比数据进行建模,以水胶比、砂率、浆骨比、粉煤灰替代比例和养护龄期5个可解释特征作为输入,抗压强度为输出。为了分析HEMNG模型在抗压强度预测中的优势,采用人工神经网络、极端梯度提升、支持向量机、随机森林等模型进行比较,并将模型迁移到全新数据中,以探究其在未知数据上的泛化能力。基于训练良好的HEMNG模型进行敏感性研究,量化3个重要特征对抗压强度的影响。结果表明:HEMNG模型采用5个可解释特征,可准确、可靠地预测抗压强度,在测试集中预测值与实际值的拟合度为0.961,均方根误差为2.704,模型预测精度和泛化能力均明显优于其他模型;将HEMNG模型迁移到新数据中,强度预测值与实际强度值较为吻合,最大绝对误差仅为7 MPa,模型表现出良好的稳健性;根据模型敏感性研究显示,存在一个最佳砂率使抗压强度达到最大;增大水胶比会降低混凝土抗压强度,最佳砂率会随水胶比增大而减小;随着浆骨比的增大,最佳砂率会表现出先增大后减小的趋势,模型能量化分析各参数对抗压强度的影响。开发的HEMNG模型为评估混凝土抗压强度提供了新的思路和方法。展开更多
Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations...Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.展开更多
Read a conversation between a biology student and his friend.So,Simon,you’re studying biology.Can you explain a little bit about it?Biology is about all the things on our world that are alive-plants,animals,as well a...Read a conversation between a biology student and his friend.So,Simon,you’re studying biology.Can you explain a little bit about it?Biology is about all the things on our world that are alive-plants,animals,as well as very small living things that we cannot see.Biology tries to explain why life is like it is.It sounds complicated.There are so many different kinds of plants and animals.展开更多
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.
文摘基于集成学习理论,首次将人工神经网络和极端梯度提升算法进行集成,提出一种全新的算法:HEMNG(hybrid ensemble model based on neural networks and gradient boosting),旨在更准确地预测混凝土抗压强度。采用303组混凝土配合比数据进行建模,以水胶比、砂率、浆骨比、粉煤灰替代比例和养护龄期5个可解释特征作为输入,抗压强度为输出。为了分析HEMNG模型在抗压强度预测中的优势,采用人工神经网络、极端梯度提升、支持向量机、随机森林等模型进行比较,并将模型迁移到全新数据中,以探究其在未知数据上的泛化能力。基于训练良好的HEMNG模型进行敏感性研究,量化3个重要特征对抗压强度的影响。结果表明:HEMNG模型采用5个可解释特征,可准确、可靠地预测抗压强度,在测试集中预测值与实际值的拟合度为0.961,均方根误差为2.704,模型预测精度和泛化能力均明显优于其他模型;将HEMNG模型迁移到新数据中,强度预测值与实际强度值较为吻合,最大绝对误差仅为7 MPa,模型表现出良好的稳健性;根据模型敏感性研究显示,存在一个最佳砂率使抗压强度达到最大;增大水胶比会降低混凝土抗压强度,最佳砂率会随水胶比增大而减小;随着浆骨比的增大,最佳砂率会表现出先增大后减小的趋势,模型能量化分析各参数对抗压强度的影响。开发的HEMNG模型为评估混凝土抗压强度提供了新的思路和方法。
基金The authors greatly thanked the financial support from the National Key Research and Development Program of China(funded by National Natural Science Foundation of China,No.2019YFA0708300)the Strategic Cooperation Technology Projects of CNPC and CUPB(funded by China National Petroleum Corporation,No.ZLZX2020-03)+1 种基金the National Science Fund for Distinguished Young Scholars(funded by National Natural Science Foundation of China,No.52125401)Science Foundation of China University of Petroleum,Beijing(funded by China University of petroleum,Beijing,No.2462022SZBH002).
文摘Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.
文摘Read a conversation between a biology student and his friend.So,Simon,you’re studying biology.Can you explain a little bit about it?Biology is about all the things on our world that are alive-plants,animals,as well as very small living things that we cannot see.Biology tries to explain why life is like it is.It sounds complicated.There are so many different kinds of plants and animals.
