Accelerating the Data Generation, Accumulation and Utilization via Machine Learning

Data science has emerged as the fourth scientific method, following experiment, theory, and computation. Since the launch of the Material Genome Initiative in 2011, data-driven approaches such as materials informatics and process informatics have been actively applied in materials. To integrate data science into traditional research, it is essential to understand research activities as a data cycle consisting of three phases : data generation, accumulation, and utilization. This cyclic process enhances the efficiency and scope of scientific research. To illustrate the impact of data science in materials research, this paper introduces Bayesian optimization for autonomous experimental system, machine learning potentials, personal databases using JSON format, and high-throughput automatic spectral analysis. These approaches contribute to the advancement of materials science through data-driven methodologies, accelerating the data cycle.