Prediction of potential well’s ground state energy using back propagation neural network

This study aims to build a Machine Learning Back Propagation Neural Network (BPNN) model to solve the Schrodinger equation for the case of potential wells. The input data is the coefficient of the Fourier representation of the well potential, and the output data is the ground state energy. The Neura...

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Bibliographic Details
Main Authors: Handayana, I. Gusti Ngurah Yudi, Sudiarta, I. Wayan, Marzuki, Afgoni, Annur
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Back propagation networks
Computer architecture
Ground state
Machine learning
Neural networks
Schrodinger equation
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Summary:This study aims to build a Machine Learning Back Propagation Neural Network (BPNN) model to solve the Schrodinger equation for the case of potential wells. The input data is the coefficient of the Fourier representation of the well potential, and the output data is the ground state energy. The Neural Network architecture consists of 5 layers (one input layer, three hidden layers, and one output layer) and eight neuron for each layer. The training results, with 74,089 data, showed the ML model to predict the ground state energy of potential wells with an accuracy of 0.9878.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0122837