arXiv subjects

Daiki Adachi

Publications and source records attributed to Daiki Adachi.

5 recordsLinked to original sources

arXiv2026

A multi-scale molecular and atomic gas view on the HII region N113 in the Large Magellanic Cloud:Evidence for high-mass star formation triggered by supersonically-colliding HI flows

The Large Magellanic Cloud (LMC) exhibits vigorous high-mass star formation, including the HII regions 30~Dor that is the most active site of star formation in the local group. The present paper focuses on the Giant Molecular Cloud (GMC) in the HII region N113 in the central part of the LMC. Based on the $^{12}$CO($J$ =1-0) and $^{13}$CO($J$ = 1-0) data at a resolution of approximately 0.2 pc taken with ALMA+APEX, we reveal that the GMC consists of two filamentary structures each of approximately 10 pc in length, forming a V-shape pattern with a vertex angle of 90 degrees. The filamentary structures host high-mass young stellar objects in gravitationally bound dense gas. Large-scale HI gas data covering 100 pc reveal two distinct velocity components separated by more than 40 km s$^{-1}$, that correspond to the low velocity (L-) and disk (D-) HI components of the LMC. The L-component appears to be located in a cavity-like distribution of the D-component, and the CO filaments are positioned at the cavity's edge. We find evidence for the L-component to fit the cavity by a 53 pc displacement, and suggest that collisional compression of the HI gas during the last 1.3 Myr triggered the GMC formation and the high-mass star formation. This lends support for the large scale collision driven by the tidal interaction is playing a role in evolution of interstellar medium in N113.

astro-ph.GA↗

arXiv2020

Bond-weighted Tensor Renormalization Group

We propose an improved tensor renormalization group (TRG) algorithm, the bond-weighted TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on the edges of the tensor network. We show that BTRG outperforms the conventional TRG and the higher-order tensor renormalization group with the same bond dimension, while its computation time is almost the same as that of TRG. Furthermore, BTRG can have non-trivial fixed-point tensors at an optimal hyperparameter. We demonstrate that the singular value spectrum obtained by BTRG is invariant under the renormalization procedure in the case of the two-dimensional Ising model at the critical point. This property indicates that BTRG performs the tensor contraction with high accuracy while keeping the scale-invariant structure of tensors.

cond-mat.stat-mech↗

arXiv2019

Anisotropic Tensor Renormalization Group

We propose a new tensor renormalization group algorithm, Anisotropic Tensor Renormalization Group (ATRG), for lattice models in arbitrary dimensions. The proposed method shares the same versatility with the Higher-Order Tensor Renormalization Group (HOTRG) algorithm, i.e., it preserves the lattice topology after the renormalization. In comparison with HOTRG, both of the computation cost and the memory footprint of our method are drastically reduced, especially in higher dimensions, by renormalizing tensors in an anisotropic way after the singular value decomposition. We demonstrate the ability of ATRG for the square lattice and the simple cubic lattice Ising models. Although the accuracy of the present method degrades when compared with HOTRG of the same bond dimension, the accuracy with fixed computation time is improved greatly due to the drastic reduction of the computation cost.

cond-mat.stat-mech↗

arXiv2018

Search for Common Minima in Joint Optimization of Multiple Cost Functions

We present a novel optimization method, named the Combined Optimization Method (COM), for the joint optimization of two or more cost functions. Unlike the conventional joint optimization schemes, which try to find minima in a weighted sum of cost functions, the COM explores search space for common minima shared by all the cost functions. Given a set of multiple cost functions that have qualitatively different distributions of local minima with each other, the proposed method finds the common minima with a high success rate without the help of any metaheuristics. As a demonstration, we apply the COM to the crystal structure prediction in materials science. By introducing the concept of data assimilation, i.e., adopting the theoretical potential energy of the crystal and the crystallinity, which characterizes the agreement with the theoretical and experimental X-ray diffraction patterns, as cost functions, we show that the correct crystal structures of Si diamond, low quartz, and low cristobalite can be predicted with significantly higher success rates than the previous methods.

physics.comp-ph↗

arXiv2017

Crystal Structure Prediction Supported by Incomplete Experimental Data

The prediction of material structure from chemical composition has been a long-standing challenge in natural science. Although there have been various methodological developments and successes with computer simulations, the prediction of crystal structures comprising more than several tens of atoms in the unit cell still remains difficult due to the many degrees of freedom, which increase exponentially with the number of atoms. Here we show that when some experimental data is available, even if it is totally insufficient for conventional structure analysis, it can be utilized to support and substantially accelerate structure simulation. In particular, we formulate a cost function based on a weighted sum of interatomic potential energies and a penalty function referred to as "crystallinity", which is defined using limited X-ray diffraction data. This method is applied to well-known polymorphs of $\rm{SiO_2}$ with up to 96 atoms in the simulation cell to find that it reproduces the correct structures efficiently with a very limited number of diffraction peaks. The penalty function is confirmed to destabilize the local minima of the potential energy surface, which facilitates finding the correct structure. This method opens a new avenue for determining and predicting structures that are difficult to determine by conventional methods, such as surface, interface, glass, and amorphous structures.

cond-mat.mtrl-sci↗