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Quantum Materials Lab
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A lab that discovers new materials using quantum-mechanical calculations and machine learning.
AboutAbout the lab
Opening up new materials with first-principles calculations and machine learning.
The Quantum Materials Lab conducts research on advanced materials using first-principles calculations and machine learning.
First-principles calculations can compute energies and electronic states with high accuracy from only information about the elements and crystal structure, but they require an enormous amount of computation time.
The lab speeds up first-principles calculations through machine learning and algorithmic improvements, making it possible to predict properties and search for structures efficiently.
Prof. Seko joined the lab in April 2025, and the lab has master's students and fourth-year undergraduates.
Key terms
- First-principles calculations
- A method based on quantum mechanics that calculates the energy and electronic states of a material from only information about its elements and crystal structure
- Machine learning potential
- A model trained by machine learning on the results of first-principles calculations, allowing the forces between atoms to be calculated quickly and accurately
- Materials informatics
- A technique that collects and analyzes materials data to help in the design and exploration of new materials
NewsLab news
A paper by Assistant Professor Wakai and colleagues was published in The Journal of Chemical Physics
A paper by Assistant Professor Wakai and colleagues was published in Physical Review Materials
A paper by Assistant Professor Hayashi and colleagues was published in Advanced Intelligent Discovery
A paper by Assistant Professor Hayashi and colleagues was published in Journal of the American Ceramic Society
A paper by Assistant Professor Hayashi and colleagues was published in Scientific Reports
ResearchResearch themes
The lab uses first-principles calculations and machine learning to enable efficient prediction of material properties and exploration of crystal structures, aiming to accelerate the development and optimization of next-generation materials.
*Illustrative imageDeveloping machine learning potentials
Based on the results of first-principles calculations, the lab develops machine learning potentials, which use machine learning to estimate the forces between atoms. Structural features are represented with group-theoretic methods, enabling large-scale molecular dynamics simulations with accuracy close to that of first-principles calculations.
*Illustrative imageSearching for crystal structures with machine learning
For multicomponent systems with unknown structures, high-pressure phases, and systems with many atoms, it is difficult to find the truly stable structure. Machine learning potentials make it possible to efficiently calculate the energies of a huge number of crystal structures, enabling structure searches over a wide range.
*Illustrative imageHigh-accuracy first-principles thermodynamic calculations
The lab develops methods that use first-principles calculations to obtain thermodynamic quantities such as heat capacity and free energy, including the effects of temperature. By incorporating the effects of lattice vibrations and atomic configurations, the lab applies these methods to problems in materials science such as phase diagrams and phase transitions.
*Illustrative imageMaterials informatics
Beyond collecting information on material structures and properties into databases, the lab develops techniques for rationally estimating properties and for automatically predicting optimal compositions, aiming to apply them to the design and exploration of a wide variety of materials.
MembersFaculty
The members of the Quantum Materials Lab consist of faculty, teaching staff, and master's and undergraduate students.
- ProfessorAtsuto Seko
- Assistant ProfessorHiroyuki Hayashi
- Assistant ProfessorHayato Wakai
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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: “Samsonite crystal structure (model ball and stick)” Ra'ike (Wikipedia) / CC0 (cropped)
- Developing machine learning potentials: “Visual Molecular Dynamics visualization” Toughpkh / Public domain (cropped)
- Searching for crystal structures with machine learning: “Carbon lattice diamond” GYassineMrabetTalk✉ This structure was created with PyMOL. / Public domain (cropped)
- High-accuracy first-principles thermodynamic calculations: “Fig 1b of NASA TM X-53445 - PRINCIPAL TYPES OF TITANIUM-ALLOY CONSTITUTION DIAGRAMS Beta Eutectoid” FF Schmidt and RA Wood / Public domain (cropped)
- Materials informatics: “Database-systems3” Чоймаа / CC BY-SA 3.0 (cropped)
- Related lab (Energy Chemistry Lab): “Lithium Iron Phosphate LiFePO4 Cells 400 Ah Amp Hours 3.25 Volts - 4” Yo-Co-Man / CC BY-SA 4.0 (cropped)
- Related lab (Quantum Energy Processes Lab): “Quantum dots 2” Marc Vidal / CC BY-SA 4.0 (cropped)
- Related lab (Materials Information Engineering Lab): “Statistical Ensembles (Estonian)” Nzjacobmartin / CC BY-SA 4.0 (cropped)
- Related lab (Functional Construction Lab): “Large sample AFM VEGA” AAMonitor96 / CC BY-SA 3.0 (cropped)
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