Understanding chemical and physical phenomena at the atomic level can lead to the solution of various problems,
such as improving the performance of materials and discovering the conditions for manufacturing materials.
I am interested in understanding these phenomena and applying that understanding to the design of molecules and materials.
My tools are primarily computational simulations based on quantum chemistry, but there are many different types of simulations.
In order to get a resonable guess, we need to consider the advantages and disadvantages of each method, and the application situations,
and make appropriate choice possible.
On the other hand, just obtaining the correct atomic image is not enough to design a material.
We have to deal with the diversity of chemistry.
Traditionally, the only tools to deal with this diversity have been specialized knowledge and intuition.
The recent application of machine learning may be one of them.
