ATOMIZATION ENERGY PREDICTION USING MACHINE LEARNING
Maju Sumanto, Muhamad Abdulkadir Martoprawiro, Atthar Luqman Ivansyah
Bandung Institute of Technology (ITB) - Computational Science
Abstract
Machine Learning is an artificial intelligence system, where the system has the ability to learn automatically from experience without being explicitly programmed. The learning process from Machine Learning starts from observing the data and then looking at the pattern of the data. The main purpose of this process is to make computers learn automatically. In this study, we used machine learning to predict molecular atomization energy. We use two methods namely Neural Network and Extreme Gradient Boosting. Both methods have several parameters that must be adjusted so the predicted value of the atomization energy of the molecule has the lowest possible error. We are trying to find the right parameter values for both methods. For the neural network method, it is quite difficult to find the right parameter value because it takes a long time to train the model of the neural network to find out whether the model is good or bad, while for the Extreme Gradient Boosting method the time needed to train the model is shorter, so it is quite easy to find the right parameter values for the model. This study also looked at the effects of the modification on the dataset with the output transformation of normalization and standardization then removing molecules containing Br atoms and changing the entry in the Coulomb matrix to 0 if the distance between atoms in the molecule exceeds 2 angstrom.
Keywords: Machine Learning, Neural Network, Extreme Gradient Boosting, Atomization Energy, Molecule.
Topic: Artificial Intelligent and soft computing