Online forecasting method for quickly forecasting organic-inorganic hybrid perovskite band gap based on machine learning
A machine learning and perovskite technology, applied in nuclear methods, genetic models, genetic rules, etc., can solve problems such as high cost and long time consumption, and achieve the effects of improving efficiency, avoiding blindness, and saving time and resources
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Embodiment 1
[0037] see figure 1 , an online prediction method for quickly predicting the band gap of organic-inorganic hybrid perovskite based on machine learning, including the following steps:
[0038] 1) Create a sample set:
[0039] Collect the chemical formulas and corresponding band gap values of organic-inorganic hybrid perovskite materials from the database as data set samples for machine learning;
[0040] 2) Generate a descriptor:
[0041] Using the collected data, calculate the physical and chemical properties of the A-site organic cation according to the chemical formula and combine the atomic parameters to generate a descriptor, and delete the samples with missing values;
[0042] 3) Divide training set and test set:
[0043] The data set samples obtained in said step 1) are randomly divided into training set and test set;
[0044] 4) Select the optimal feature subset for modeling:
[0045] Taking the bandgap collected in said step 1) as the target variable, use the ge...
Embodiment 2
[0054] This embodiment is basically the same as Embodiment 1, and the special features are as follows:
[0055] The online prediction method based on machine learning to quickly predict the band gap of organic-inorganic hybrid perovskite, in the step 4), the method of the support vector machine algorithm is as follows:
[0056] The support vector machine algorithm is based on the ε-insensitive function and kernel function algorithm; if the fitted mathematical model is expressed as a certain curve in multidimensional space, the result obtained according to the ε-insensitive function is the " εpipe”; among all sample points, only the part of points distributed on the “pipe wall” determines the position of the pipe; this part of the training samples becomes the “support vector”.
[0057] In this embodiment, the band gap prediction model of organic-inorganic hybrid perovskite materials is established through the support vector machine regression algorithm, which has high accuracy an...
Embodiment 3
[0059] This embodiment is basically the same as the above-mentioned embodiment, and the special features are as follows:
[0060] An online forecast application program for quickly predicting the band gap of organic-inorganic hybrid perovskite based on machine learning, including the following steps:
[0061] 1) Collect the chemical formulas and corresponding band gap values of organic-inorganic hybrid perovskite materials from the database as data set samples; the band gap values of some organic-inorganic hybrid perovskite materials are shown in Table 1:
[0062] Table 1. Data sample set of some organic-inorganic hybrid perovskite chemical formulas and band gap values
[0063] chemical formula EgV chemical formula EgV CH 5 N 2 SnF 3
3.52 h 4 NOG 3
4.67 CH 3 (CH 2 ) 2 NH 3 PbBr 3
3.08 CH 6 N 3 PB 3
2.08 CH 3 (CH 2 ) 3 NH 3 GeCl 3
3.59 (CH 3 )4NSnI 3
2.33 CH 6 NCaCl 3
...
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