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Computational Quantum Materials Science Lab
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A research group that explores the atomic structure of complex materials through large-scale first-principles calculations.
AboutAbout the lab
Solving huge systems from first principles
The lab uses first-principles calculations to clarify the atomic-scale structures and properties of matter and materials.
First-principles calculations are computationally very expensive, which has made it difficult to handle large, complex systems containing many atoms.
Together with a group in the UK, the lab has developed its own computational method and program that can handle large systems, and uses them to study defects, surfaces and interfaces, and nanostructured materials.
Students can join the lab from graduate school.
Research is carried out at the National Institute for Materials Science (NIMS) in Tsukuba, Ibaraki.
Key terms
- First-principles calculations
- A research method that quantitatively determines the atomic-scale structure and properties of matter and materials by calculation. The computational cost is enormous.
- Order-N method
- A computational method that CONQUEST uses together with a localized-orbital method. It makes first-principles calculations of huge systems, which are normally out of reach, possible.
- Gallium nitride
- A power semiconductor material. Written GaN.
ResearchResearch themes
Together with a research group in the UK, the lab develops the large-scale first-principles program CONQUEST, and uses it to investigate the electronic states and structures of huge systems that conventional first-principles calculations cannot handle. The lab has also developed a machine-learning method for analyzing the complex local structures that appear in the calculations, and applies it to materials such as semiconductors.
Developing a large-scale first-principles program
Together with a UK research group, the lab has developed CONQUEST, a program that can perform electronic structure analysis, structural optimization, and molecular dynamics even for huge systems, using order-N methods and an original localized-orbital method. CONQUEST was released in January 2020 and is free to use.
Reading complex structures with machine learning
The lab applies large-scale first-principles molecular dynamics with CONQUEST to the structures of surfaces, defects, and amorphous materials. It has proposed an unsupervised machine-learning method for analyzing the various local structures that appear in the simulations, and is testing its effectiveness by applying it to a variety of materials.
Modeling defect structures in gallium nitride
Using large-scale first-principles calculations, the lab searches for the various defect structures formed when magnesium is ion-implanted into gallium nitride, a power semiconductor material, as well as the composite structures formed by magnesium atoms, and examines their stability. The local structures that appear are classified and analyzed with a method based on unsupervised machine learning.
Materials research with machine learning
The first-principles quantum materials group at NIMS conducts materials science research using machine learning on diverse materials data, including results from simulations, synthesis, and measurements. The group is also developing new materials informatics methods.
MembersFaculty
- Visiting ProfessorTsuyoshi Miyazaki
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