arXiv subjects

Satoru Ohuchi

Publications and source records attributed to Satoru Ohuchi.

4 recordsLinked to original sources

arXiv2026

Constrained Optimization of Higher-Order Cluster-Expansion Hamiltonians for Alloys Using Simulated Bifurcation

Identifying ground-state and low-energy atomic configurations is a central problem in alloy design. The cluster-expansion (CE) method represents configurational energetics on a fixed lattice as an effective Hamiltonian; for binary alloys, higher-order CE models become polynomial Ising Hamiltonians. Using Au-Cu as a model binary alloy, we formulate cubic and quartic cluster-expansion Hamiltonians as penalty-augmented polynomial unconstrained binary optimization (PUBO) problems under fixed-composition constraints. We optimize these PUBO problems using SQBM+, a simulated-bifurcation-based solver that can treat higher-order polynomial binary objectives directly. This direct PUBO treatment avoids the need to construct an explicit quadratic reformulation with auxiliary variables. Composition constraints are imposed through quadratic penalty terms, whose weights are estimated from derivative coefficients of the continuous relaxation of the CE objective. Benchmark calculations for systems up to 2048 atoms show that SQBM+ robustly obtains low-energy feasible configurations for cubic CE models and remains effective for many quartic instances. Formation-energy convex hulls constructed from the optimized configurations recover the CuAu and Cu3Au ordering trends and reveal finite-size effects at off-stoichiometric compositions. These results demonstrate simulated bifurcation as a practical route to constrained higher-order CE optimization for alloy configuration search.

cond-mat.mtrl-sci↗

arXiv2025

Optimal elemental configuration search in crystal using quantum approximate optimization algorithm

Optimal elemental configuration search in crystal is a crucial task to discovering industrially important materials such as lithium-ion battery cathodes. In this paper we present application of quantum approximate optimization algorithm, the representative near-term quantum algorithm for combinatorial optimization, to finding the most stable elemental configuration in a crystal, using Au-Cu alloys as an example. After expressing the energy of the crystal in the form of the Ising model through the cluster expansion method combined with first-principles calculations, we numerically perform QAOA with three types of parameter optimization. As a result, we have demonstrated that the optimal solution can be sampled with a high probability for crystals containing up to 32 atoms. Our results could pave the way for optimal elemental configuration search in a crystal using a near-term quantum computer.

quant-ph↗

arXiv2024

SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning

Neural Network Potentials (NNPs) have attracted significant attention as a method for accelerating density functional theory (DFT) calculations. However, conventional NNP models typically do not incorporate spin degrees of freedom, limiting their applicability to systems where spin states critically influence material properties, such as transition metal oxides. This study introduces SpinMultiNet, a novel NNP model that integrates spin degrees of freedom through multi-task learning. SpinMultiNet achieves accurate predictions without relying on correct spin values obtained from DFT calculations. Instead, it utilizes initial spin estimates as input and leverages multi-task learning to optimize the spin latent representation while maintaining both $E(3)$ and time-reversal equivariance. Validation on a dataset of transition metal oxides demonstrates the high predictive accuracy of SpinMultiNet. The model successfully reproduces the energy ordering of stable spin configurations originating from superexchange interactions and accurately captures the rhombohedral distortion of the rocksalt structure. These results pave the way for new possibilities in materials simulations that consider spin degrees of freedom, promising future applications in large-scale simulations of various material systems, including magnetic materials.

cond-mat.mtrl-sci↗

arXiv2023

Accelerating Optimal Elemental Configuration Search in Crystal using Ising Machine

This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. We have successfully obtained reasonable solutions for crystal structures consisting of over 10,000 atoms. Notably, we have also obtained plausible solutions for optimization problems with constrained solutions, such as situations where the composition ratio of atomic species is predetermined. These findings suggest that Ising machines can be valuable tools for addressing materials science challenges.

cond-mat.mtrl-sci↗