Abstract: An automated chemical formulation apparatus includes: a data reception unit that receives an input chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof; a predicting model generation unit that trains a first machine learning model using the input chemical material dataset to generate a predicting model for predicting a chemical formulation based on target property information of a target material; and a formulation prediction unit that sets a boundary condition based on the input chemical material dataset, generates a new input dataset including at least one of chemical material information, chemical composition information, and chemical formulation information within the boundary condition, inputs the new input dataset to the predicting model, and sets predetermined one or more pieces of target property information to perform prediction, thereby outputting a first group of chemical formulation da

Abstract: A data generation apparatus includes a data input unit that inputs an initial dataset, a data preprocessing unit that normalizes the initial dataset and splits the normalized initial dataset into a initial training dataset and a initial test dataset, a learning model generation unit that trains a first machine learning model using the initial training dataset and optimizes a hyperparameter of the first machine learning model using a predetermined number of cross-validations, thereby generating a first learning model, a validation unit that validates the generated first learning model using the initial test dataset, a semi-synthetic data generation unit that selects and generates a new parameter within a boundary space defined by the initial dataset and performs data prediction using the first learning model based on the new parameter to generate a semi-synthetized dataset, and a database that stores the initial dataset and the semi-synthetized dataset.

Abstract: An apparatus for constructing a library for deriving a material composition using empirical result, which enables acceleration of research on the material-properties relationship. By applying the empirical results of the material composition, missing data of the material compositions can be statistically calculated by using supervised non-linear imputation techniques. The completed composition information of the materials is passed as an input of machine learning material-properties relationship prediction.