Ultimate Surface Creation by Manupulating and Constructing with Atoms
RESEARCH OUTLINE
Nanomaterials such as diamond-like carbon (DLC), carbon nanotubes, and graphene exhibit unique and exceptional properties that are not found in conventional materials. By precisely controlling their structures at the atomic level and tailoring their properties as needed, it becomes possible to create "surfaces that can be designed at the atomic scale and whose properties are defined at the nanoscale"—in other words, the "ultimate surface." Surface modification technologies utilizing these nanomaterials are expected to fundamentally transform the design of next-generation functional surfaces and drive innovation across a wide range of fields.
POINT
This research aims to create the "ultimate surface" by precisely controlling the structure and properties of nanomaterials. To achieve this, we leverage AI simulations, including machine learning, to accelerate nanoscale material design. By integrating large-scale data analysis with first-principles calculations, we predict optimal atomic arrangements and process conditions, thereby establishing an innovative surface modification technology that enables precise tuning of nanomaterial properties.
Diamond-Like Carbon (DLC) films are a class of amorphous hard carbon thin films that exhibit an intermediate structure between diamond and graphite. They offer excellent properties such as high durability, wear resistance, low friction, chemical stability, and biocompatibility, making them promising for a wide range of applications across various fields.
Low-dimensional nanomaterials are novel materials with atomic-scale thickness or width, exhibiting unique properties distinct from conventional three-dimensional materials such as metals and plastics. For example, carbon nanotubes (CNTs) have an elongated one-dimensional structure, offering exceptional strength, light weight, and high electrical conductivity. Graphene, a two-dimensional material only one atomic layer thick, demonstrates extraordinary properties, including conductivity surpassing that of copper. These low-dimensional materials are gaining attention as key enablers of innovation in electronics and energy technologies.
Van der Waals heterostructures (vdWH) are novel architectures formed by stacking and integrating different nanomaterials, enabling precise control of electrical and optical properties through layer combinations and stacking angles. These structures rely on van der Waals forces for interlayer bonding, preserving the intrinsic properties of each layer while creating new functionalities. Our laboratory pioneers the development of interdimensional van der Waals heterostructures by not only combining two-dimensional materials such as graphene and hexagonal boron nitride (hBN) but also integrating one-dimensional materials like carbon nanotubes and boron nitride nanotubes (BNNT) and three-dimensional materials such as diamond-like carbon (DLC).
By integrating theoretical computational science with data science through materials informatics, we aim to unravel the complex underlying principles at vdWH interfaces and evaluate their physical properties to explore unprecedented nanomaterials with groundbreaking characteristics. Additionally, we employ machine learning simulations that incorporate molecular dynamics calculations into first-principles calculations to derive optimal Neural Network potentials, facilitating the elucidation of vdWH growth mechanisms. The computations are performed using TSUBAME4.0, housed at the Suzukakedai Campus, which boasts computational speed second only to "Fugaku" among existing supercomputers in Japan.