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Interactive yield optimization method and system based on recurrent neural network

A cyclic neural network, interactive technology, applied in the field of chemical reaction yield optimization, can solve problems such as missing reaction conditions, achieve optimal reaction yield, solve experimental design problems, and reduce costs.

Active Publication Date: 2022-07-22
烟台国工智能科技有限公司
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

However, this often misses the optimal reaction conditions

Method used

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  • Interactive yield optimization method and system based on recurrent neural network
  • Interactive yield optimization method and system based on recurrent neural network
  • Interactive yield optimization method and system based on recurrent neural network

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Embodiment 1

[0043] An interactive yield optimization method based on a cyclic neural network of the present invention, such as figure 1 shown, including the following steps:

[0044] S100. For the current chemical reaction, obtain a variety of experimental condition parameters, for numerical experimental condition parameters, encode them by delimiting a numerical value range, and for non-numerical experimental condition parameters, encode them by SMILES expression, Obtain the experimental condition parameters after encoding;

[0045]S200, taking the encoded experimental condition parameters as input, and simulating the current chemical reaction through the mixed Gaussian density function to obtain the reaction yield;

[0046] S300, constructing a chemical reaction model based on a cyclic neural network model, the chemical reaction model is used to predict and output the experimental condition parameters of the next round based on a historical data set, current experimental condition para...

Embodiment 2

[0092] The present invention is an interactive yield optimization system based on a cyclic neural network, comprising a data collection module, an initial data construction module, a model construction module, and a model training module. The system is used to implement a recurrent neural network-based interactive yield optimization method disclosed in Example 1.

[0093] For the current chemical reaction, the data collection module is used to obtain a variety of experimental condition parameters. For numerical experimental condition parameters, they are coded by delimiting the numerical value range. For non-numerical experimental condition parameters, the SMILES expression is used. Encoding is performed to obtain the post-encoding experimental condition parameters.

[0094] In this embodiment, the data collection module is used to obtain experimental data and perform preprocessing. As a specific implementation, the experimental conditions and auxiliary substances participatin...

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Abstract

The invention discloses an interactive yield optimization method and system based on a recurrent neural network, belongs to the technical field of chemical reaction yield optimization, and aims to solve the technical problem of how to obtain a relatively high chemical reaction yield on the premise of reducing the experiment cost. Comprising the following steps: acquiring various experimental condition parameters; simulating a current chemical reaction through a mixed Gaussian density function to obtain a reaction yield; constructing a chemical reaction model based on the recurrent neural network model; and initializing a historical data set, training the chemical reaction model by taking the encoded experimental condition parameters and the corresponding chemical reaction yield as current experimental condition parameters and corresponding reaction yield, outputting experimental condition parameters of the next round as target experimental parameters, and performing a chemical experiment based on the target experimental condition parameters. The obtained reaction yield is used as a target reaction yield; and under the condition that the target reaction yield reaches a threshold value, carrying out multiple rounds of training on the chemical reaction model.

Description

technical field [0001] The invention relates to the technical field of chemical reaction yield optimization, in particular to an interactive yield optimization method and system based on a cyclic neural network. Background technique [0002] In a chemical reaction, in addition to the reactants themselves, there are many factors that will affect the yield of the chemical reaction. In order to obtain a higher reaction yield, experts in the chemical field need to go through a lot of experiments, which inevitably requires a lot of time and money. Usually at the beginning of the experiment, some experimental plans are designed to reduce the number of experiments and reduce the cost of experiments. However, this often misses optimal reaction conditions. [0003] How to obtain a relatively high chemical reaction yield under the premise of reducing the experimental cost is a technical problem that needs to be solved. SUMMARY OF THE INVENTION [0004] The technical task of the p...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G16C20/10G06F111/10
CPCG06F30/27G06N3/084G16C20/10G06F2111/10G06N3/044
Inventor 柳彦宏戴开洋张浩
Owner 烟台国工智能科技有限公司
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