Computer aided design system for predicting energetic molecule based on machine learning performance
A technology of molecular computer and machine learning, applied in the direction of molecular computer, calculation, calculation model, etc., can solve the problems of consumption, waste of time and labor cost, hinder the efficiency of research and development of new energetic molecules, etc., and achieve great flexibility and scalability Effect
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Embodiment 1
[0050] Figure 5 is a process for predicting the performance data of a single known energetic molecule RDX. The first step is to input the molecular structure of RDX represented by the SMILES code, the second step is to optimize the structure and calculate the descriptor, and the third step is to select the model generated by the Kernel Ridge Regression algorithm (KRR) to evaluate the density, detonation velocity and detonation pressure performance of RDX. predict. Compared with the experimental value, the error of the predicted result is very small.
Embodiment 2
[0052] Image 6 It is a flow for the whole molecular space generation and performance prediction based on benzene ring and amino and nitro substituents. The user only needs to input the structural formulas of benzene ring, nitro group and amino group expressed in SMILES format, and the rapid molecular generation module will automatically generate 91 molecular structures without repetition, and then the molecular descriptor module will optimize the 91 molecular structures and calculate the descriptors. Then, the system will predict the performance of these 91 molecules according to the performance that the user wants to predict and the model generated by the algorithm used, such as the Kernel Ridge Regression (KRR) model. The whole process only takes a few minutes, and the speed Far beyond traditional calculation methods, and still maintain high accuracy.
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