Computer aided design system for predicting energetic molecule based on machine learning performance

Abstract

The invention discloses a computer aided design system for predicting energetic molecules based on machine learning performance, and belongs to the technical field of computer aided design systems. The system comprises: a molecule rapid generation module, which is used for generating all possible molecular structural formulas according to permutation and combination of a molecular mother ring anda substituent group input by a user, and removing a repeated structure; a molecular data set module used for recording reported energetic molecular performance data, and the energetic molecular performance data being mainly used as a training set of the machine learning model training module; a molecular descriptor generation module used for calculating molecular descriptors of molecules generatedby the molecular rapid generation module or molecules in the molecular data set module or molecules input by a user; a machine learning model training module used for training and storing a model byadopting a machine learning algorithm according to the data of the molecular descriptor set; and a performance prediction module used for reading the model and carrying out performance prediction on the molecules generated by the molecule rapid generation module or the molecules input by the user.

Description

technical field [0001] The invention relates to a computer-aided design system, in particular to a computer-aided design system for predicting the properties of energetic molecules based on machine learning methods. The system can assist researchers to generate all possible molecular structures according to specific parent rings and substituent arrangements, and realize performance prediction based on machine learning methods. Background technique [0002] If Nobel's development of "Dynamat" is taken as a starting point, the development of modern energetic materials has a history of nearly 200 years. During this period, researchers successively discovered many new types of high-performance energetic molecules, such as RDX, HMX, CL-20, etc., but more attempts ended in failure, and the design and development of energetic molecules still need to consume a lot of energy. time and labor costs. The reason is that, on the one hand, the mainstream research and development model of...

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Problems solved by technology

During this period, researchers successively discovered many new types of high-performance energetic molecules, such as RDX, HMX, CL-20, etc., but more attempts ended in failure, and the design and development of energetic molecules still need to consume a lot of energy. time and labor costs

The reason is that, on the one hand, the mainstream research and development model of energetic molecules is still the traditional "trial and error method", which undoubtedly wastes a lot of time and labor costs, and seriously hinders the research and development efficiency of new energetic molecules.

On the other hand, although researchers can now predict the performance of energetic molecules to be developed, so as to evaluate whether the energetic molecules have high application potential and then synthesize them, such performance prediction usually requires prior knowledge Molecular structure, density, enthalpy of formation and other data, and these data are usually obtained through a large number of time-consuming first-principles calculations, so this "drawing and playing" research and development model is also difficult to achieve high-pass for energetic molecules. Volume Forecast Screening Assessment

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