Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate a...Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
基金financially supported by the Ministry of Science and Technology of China (2017YFA0204503 and 2018YFA0703200)the National Natural Science Foundation of China (52121002,U21A6002 and 22003046)+1 种基金the Tianjin Natural Science Foundation (20JCJQJC00300)“A Multi-Scale and High-Efficiency Computing Platform for Advanced Functional Materials”program,funded by Haihe Laboratory in Tianjin (22HHXCJC00007)。
文摘Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
