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Materials Modelling Lab
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The lab uses atomic-scale simulations to study how microstructures form in metals.
AboutAbout the lab
Uncovering how materials form, starting from the motion of atoms
Materials Modelling Lab (Shibuta Lab) studies how materials form and what properties they have, using computer simulation.
Centered on molecular dynamics, which calculates the motion of each individual atom, the lab investigates phenomena such as how liquid metal solidifies.
The lab also carries out large-scale calculations on supercomputers and develops new methods that combine simulation with machine learning.
In addition, the lab studies how to understand life-like phenomena using the concepts of artificial life.
Key terms
- molecular dynamics
- A simulation that calculates the forces acting on each individual atom and tracks the motion of the atoms on a computer
- nucleation
- The first formation of tiny crystal seeds (nuclei) when a liquid cools and solidifies
- machine learning
- A technology in which a computer learns patterns from large amounts of data and uses them for tasks such as prediction
NewsLab news
Published a paper on hydride formation pathways investigated with machine learning potentials
Published a paper on controlling the disordered motion of self-oscillating gels using artificial life methods
Published a collaborative paper on grain boundary structure and segregation in aluminum alloys
Published a collaborative paper investigating the strength of aluminum grain boundaries using first-principles calculations and machine learning
Published a collaborative paper on an analysis method for identifying nanoparticle shapes
ResearchResearch themes
Using simulations centered on molecular dynamics, the lab investigates, at the atomic level, how crystal nuclei form when metallic materials solidify (nucleation) and how microstructures grow. It also develops new computational methods for predicting material properties, combining them with techniques such as machine learning and data assimilation.
*Illustrative imageMetadynamics Simulations of Metal Processes
Using a method called metadynamics, the lab directly calculates the free-energy landscape for the formation of crystal nuclei, which is difficult to obtain with conventional molecular dynamics. It also calculates how the components of an alloy separate (segregate) as it solidifies.
*Illustrative imageUnderstanding Life Phenomena through Artificial Life
Using the artificial life approach, which seeks to understand life through mechanisms that imitate it, the lab computationally studies how a self-oscillating gel recovers from disordered motion in response to a stimulus (joint work with Prof. Yoshida's group).
*Illustrative imageCombining Molecular Dynamics and Machine Learning
The lab proposes methods that combine machine learning, such as deep learning, with molecular dynamics to predict changes in which many atoms act cooperatively, as well as transitions in the microstructure of materials.
*Illustrative imageSolidification and Microstructure Formation Seen through Large-Scale Computation
Using a GPU supercomputer to handle enormous numbers of atoms, the lab reproduces the entire process from crystals forming in cooled liquid metal to the growth of the microstructure (joint work with Kyoto Institute of Technology and Hokkaido University).
MembersFaculty
Members of Materials Modelling Lab include faculty, technical staff, postdoctoral researchers, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorYasushi ShibutaPublications ↗
- Assistant ProfessorRyuhei SatoPublications ↗
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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: “Color Metallography of Engineering Alloys” OndrejAmbroz / CC BY 4.0 (cropped)
- Metadynamics Simulations of Metal Processes: “Plot of classical nucleation theory prediction for the free energy of a nucleus as a function of radius” Richard Sear / CC BY-SA 3.0 (cropped)
- Understanding Life Phenomena through Artificial Life: “Belousov Zhabotinsky reaction ( )” Stephen Morris from Toronto, Canada / CC BY 2.0 (cropped)
- Combining Molecular Dynamics and Machine Learning: “The neural network model that generates the potential energy surface for MD simulation” Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu & John Z. H. Zhang / CC BY 4.0 (cropped)
- Solidification and Microstructure Formation Seen through Large-Scale Computation: “Silver crystal” Alchemist-hp (talk) (www.pse-mendelejew.de) / CC BY-SA 3.0 de (cropped)
- Related lab (Abe Lab): “Stohrem” Materialscientist / CC BY-SA 3.0 (cropped)
- Related lab (Nambu Lab): “EBSD orientation map of ferrous lath martensite” Tiberio / CC BY-SA 4.0 (cropped)
- Related lab (Watanabe Lab): “Scanning Tunneling Microscope Structure ( )” National Institute of Standards and Technology / Public domain (cropped)
- Related lab (Edagawa Lab (Strength and Physical Properties of Materials)): “Icosahedrite Diffraction Pattern” Paul Steinhardt / CC BY-SA 4.0 (cropped)
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