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Nano Functionality Prediction Lab
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The lab uses computation and theory to understand the functions of nanomaterials.
AboutAbout the lab
Understanding the functions of nanomaterials through computation and theory
The lab conducts research using computer simulations and condensed matter theory to understand the functions of materials at the nanoscale and to find ways to bring out better properties.
It also develops new simulations that apply data science, including machine learning.
It also studies the theory of spintronics, which makes use of the spin of electrons.
The lab is a cooperating laboratory of the Department of Materials Engineering Science (Materials Physics area), Graduate School of Engineering Science, Osaka University, and accepts students in both the master's and doctoral programs.
Key terms
- First-principles calculation
- A method that calculates the properties of real materials with high accuracy based on quantum mechanics. It is computationally expensive
- Machine learning potential
- A machine learning model that, in place of first-principles calculations, returns the energy and the forces acting on atoms from their arrangement
- Topological data analysis
- An analysis method that uses mathematical topology to quantify "hole" structures, such as loops and voids, determined by how the data are connected
- Spin Hall effect
- A phenomenon that uses spin-orbit interaction to manipulate the spin of electrons without a magnetic field
NewsLab news
A paper was published in Proceedings of the National Academy of Sciences, with a press release
An undergraduate fourth-year student received the Kusumoto Award
A paper first-authored by a master's student was published in Science and Technology of Advanced Materials: Methods
A paper on applying data science was published in Nature Communications, with a press release
A paper on spintronics theory was published in npj Spintronics, with a press release
ResearchResearch themes
The lab conducts research using computer simulations and condensed matter theory to understand the functions of materials at the nanoscale and to find ways to bring out better properties. It also develops new simulations that apply data science, including machine learning.
*Illustrative imageApplying machine learning and data analysis
The lab aims to use neural-network machine learning potentials to reduce the cost of first-principles calculations while maintaining accuracy. It also builds a machine learning model that predicts the thermal conductivity of amorphous Si using topological data analysis.
*Illustrative imageTheory of spintronics
The lab proposed the spin accumulation coefficient, which focuses on the spin accumulation that arises at the edge of a sample, as a quantitative measure of the spin Hall effect. It investigates which materials show a "giant" effect using analytical calculations and first-principles calculations.
MembersFaculty
The lab's members consist of faculty, secretaries, doctoral, master's, and undergraduate students, and short-term visitors.As of 2026
- ProfessorEmi Minamitani
- Associate ProfessorAtsuo Shitade
- Assistant ProfessorNguyen Thi Phuong Thao
- Assistant ProfessorKumpei Shiraishi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Carbon nanorim zigzag povray” Arnero / Public domain (cropped)
- Applying machine learning and data analysis: “Example of a deep neural network” BrunelloN / CC BY-SA 4.0 (cropped)
- Theory of spintronics: “SpinHallEffect” Eleanor Holmes / CC BY-SA 4.0 (cropped)
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