Our research is focused on how to develop regression or classification models used in the chemical and material sciences. The chemical descriptors, such as ionic radius, atomic radius, electronegativity, energies, geometric parameters, etc, have been used as feature to build the models which can predict the properties accurately, with data mining approach (Support Vector Machin, Artificial neural networks, Genetic Algorithm, etc.)
Our research is focused on how to find the best conditions of preparation or the structure-property relationship of materials, in order to make experimental design for new materials preparation or to predict the physico-chemical properties of unknown materials system.
Our research is focused on how to find the structure-active relationship of molecules, in order to design new compounds with expected biological activities or predict the physico-chemical properties of unknown molecules.
Our research is focused on how to acquire the optimized conditions of processing productions, in order to achieve the good results of industrial production.
Our research is focused on how to develop the statistical models with good generalization by using chemical data mining methods including partial least square regression (PLSR), pattern recognition (PR), artificial neural networks (ANN), genetic algorithm (GA) and support vector machine (SVM), etc.