Inui Group
Research Subjects
- Developing inverse design methods to create materials and conditions with specific desired properties
- Fast data assimilation with large number of parameters
- Numerical Studies in Condensed Matter Physics
In our laboratory, we are pioneering the development of inverse analysis and design methods for computational materials research. Traditional approaches typically involve simulations with a limited set of parameters for specific substances or conditions, focusing on identifying parameter regions that achieve desired states. However, this method often falls short in discovering novel substances and states due to its reliance on predefined conditions. To overcome these limitations, we employ inverse design techniques. These begin by defining the desired properties and then identifying systems that can realize these properties. A key aspect of our research is the use of automatic differentiation, a powerful tool frequently utilized in machine learning, which allows for the control of a large number of parameters. By integrating inverse analysis and design, we aim to uncover new substances and mechanisms previously unexplored. This innovative approach holds the potential to revolutionize the field by enabling the discovery of groundbreaking materials and processes.
