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

Pending Publication Date: 2020-12-22
SHANGHAI UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the value of the band gap needs to be obtained through experiments an

Method used

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  • Online forecasting method for quickly forecasting organic-inorganic hybrid perovskite band gap based on machine learning
  • Online forecasting method for quickly forecasting organic-inorganic hybrid perovskite band gap based on machine learning
  • Online forecasting method for quickly forecasting organic-inorganic hybrid perovskite band gap based on machine learning

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0036] Example 1:

[0037] see figure 1 , an online prediction method based on machine learning to rapidly predict the band gap of organic-inorganic hybrid perovskites, including the following steps:

[0038] 1) Create a sample set:

[0039] Collect chemical formulas and corresponding band gap values ​​of organic-inorganic hybrid perovskite materials from the database as dataset samples for machine learning;

[0040] 2) Generate descriptor:

[0041] Using the collected data, calculate the physicochemical properties of the A-position organic cations according to the chemical formula and generate descriptors combined with atomic parameters, and delete the samples with missing values;

[0042] 3) Divide training set and test set:

[0043] The data set sample obtained in the step 1) is randomly divided into a training set and a test set;

[0044] 4) Select the optimal feature subset for modeling:

[0045] Taking the band gap collected in the described step 1) as the target v...

Example Embodiment

[0053] Embodiment 2:

[0054] This embodiment is basically the same as the first embodiment, and the special features are as follows:

[0055] In the online prediction method for rapidly predicting the band gap of organic-inorganic hybrid perovskite based on machine learning, 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 the kernel function algorithm; if the fitted mathematical model is expressed as a certain curve in a multi-dimensional space, the result obtained according to the ε-insensitive function is the "envelope" of the curve and the training point. ε pipeline"; among all the sample points, only that part of the points distributed on the "pipe wall" determines the position of the pipeline; this part of the training samples becomes the "support vector".

[0057] In this embodiment, a support vector machine regression algorithm is used to establish ...

Example Embodiment

[0058] Embodiment three:

[0059] This embodiment is basically the same as the above-mentioned embodiment, and the special features are as follows:

[0060] An online prediction application based on machine learning to rapidly predict the band gap of organic-inorganic hybrid perovskite, 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 chemical formulas and band gap values ​​of some organic-inorganic hybrid perovskites

[0063] chemical formula EgeV chemical formula EgeV CH 5 N 2 SnF 3

[0064] 2) Using the collected data, calculate the physicochemical properties of the A-position organic cations according to the chemical formula and generate descriptors combined with atom...

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Abstract

The invention discloses an online forecasting method for quickly forecasting an organic-inorganic hybrid perovskite band gap based on machine learning, which comprises the following steps: establishing a sample set, generating a descriptor, dividing a training set and a test set, selecting a modeled optimal feature subset, constructing a quick forecasting model, forecasting the band gap of a testset sample, developing an online forecasting application program, and rapidly predicting the organic-inorganic hybrid perovskite band gap value. According to the method, an efficient and rapid forecasting model is established through sample data from a database, an online forecasting application program for rapidly forecasting the organic-inorganic hybrid perovskite is developed, the online forecasting application program can be accessed and used through a website and a mobile phone WeChat two-dimensional code, and the method has the advantages of being simple, convenient, low in cost and environmentally friendly. By using the application program to forecast the band gap of the organic-inorganic hybrid perovskite on line, experimental researchers can be helped to avoid blindness of an experimental trial-and-error method, experimental time and cost are saved, and material research and development efficiency is improved.

Description

technical field [0001] The invention relates to the application of organic-inorganic hybrid perovskite in the field of optics. It is an online prediction method based on machine learning to predict the band gap of organic-inorganic hybrid perovskite, which is applied to the design of organic-inorganic hybrid perovskite with a specific band gap Mining new materials and high-throughput screening. Background technique [0002] Due to its stable crystal structure and unique physical and chemical properties, perovskite has gradually become a hot spot in the development and research of new materials. Organic-inorganic hybrid perovskites are important materials with promising magnetic, optical, and electrical properties due to their low cost, good adaptability, stability, and tunable electronic structure. In recent years, the photoelectric conversion efficiency of organic-inorganic hybrid perovskite solar cells has increased rapidly to more than 23%, and they are widely used as hi...

Claims

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Application Information

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IPC IPC(8): G06N3/12G06N20/10
CPCG06N3/126G06N20/10
Inventor 张诗琳李敏杰陆文聪卢天陶秋伶刘秀娟陈慧敏
Owner SHANGHAI UNIV
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