Calculation of the Total Energy of a Carbon Molecule Using First-Order Perturbation Theory and Machine Learning

Abstract

we developed a machine-learning approach for calculating the total energy of a carbon molecule. We compared its performance with numerical integration and two established machine-learning methods: a Kolmogorov–Arnold network and a random forest. We evaluated the accuracy of the methods using the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). We compared the methods in terms of accuracy, computational efficiency, and applicability to different calculation tasks.