11 citations · 33 across the 24 of their papers we have counts for
25 papers
Angular compliance, not amplitude, governs the dielectric response of BaTiO3
Ryotaro Sahashi, Po-Yen Chen, Teruyasu Mizoguchi
Soft-mode theory specifies a frequency, not what a local polar unit does. What real-space motion underlies the dielectric response of BaTiO3 therefore remains unclear. Using electr…
Transient Detour and Cooperative Oxygen Exchange in the Polarization Switching of Ferroelectric Hf0.5Zr0.5O2
Ryotaro Sahashi, Po-Yen Chen, Teruyasu Mizoguchi
Hafnium zirconium oxide (HZO) has attracted significant attention as a core material for next-generation non-volatile memories due to its excellent ferroelectricity in the ultra-th…
Toward Auditable AI Scientists: A Hypothesis Evolution Protocol for LLM Agents
Izumi Takahara, Teruyasu Mizoguchi
Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool us…
Intrinsic Defect Energetics and Fluorine Doping Effects in Li2CO3 and Li2O2: A First-Principles Study
Youjeong Choi, Tasuku Sugiura, Keisuke Mukai +3
Lithium carbonate, Li2CO3, is a thermodynamically stable carbonate phase whose defect energetics are closely related to its stability and decomposition behavior in various lithium-…
Transition from Homogeneous to Domain-Wall-Mediated Polarization Switching in BaTiO3: A Machine-Learning Molecular Dynamics Study
Po-Yen Chen, Teruyasu Mizoguchi
Polarization switching in ferroelectric BaTiO3 can proceed through fundamentally different mechanisms - yet the conditions that determine which pathway is realized remain poorly un…
Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction
Shuichiro Ozawa, Izumi Takahara, Teruyasu Mizoguchi
Large language models (LLMs) are increasingly applied to materials science. However, the relationship between prediction accuracy, input representation, and model scale remains unc…
Inverse Materials Design via Joint Generation of Crystal Structures and Local Electronic Descriptors
Ibuki Okuda, Izumi Takahara, Teruyasu Mizoguchi
Inverse design of inorganic crystals, in which structures are generated to satisfy a target property while preserving diversity and physical plausibility, remains more demanding th…
Ultrafast Sliding Ferroelectric Switching in Bilayer Hexagonal Boron Nitride Revealed by Deep Learning Molecular Dynamics
Yinan Wang, Poyen Chen, Teruyasu Mizoguchi
Sliding ferroelectricity in bilayer hexagonal boron nitride (h-BN) offers compelling prospects for next-generation non-volatile memory, yet the atomistic dynamics of electric-field…
Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES
Yinan Wang, Arpita Varadwaj, Teruyasu Mizoguchi +1
Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine…
Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials
Po-Yen Chen, Teruyasu Mizoguchi
Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations throu…
Twist-Angle Engineering of Moiré Potentials for High-Performance Ionics in Bilayer Graphene
Gen Fukuzawa, Yebin Lee, Teruyasu Mizoguchi
Controlling ion transport is a fundamental challenge for advanced energy storage. Bilayer graphene offers a unique platform for modulating ion diffusion via twist-angle-dependent m…
Origin of Reduced Coercive Field in ScAlN: Synergy of Structural Softening and Dynamic Atomic Correlations
Ryotaro Sahashi, Po-Yen Chen, Teruyasu Mizoguchi
Among wurtzite-type ferroelectrics, scandium-doped aluminum nitride (ScAlN) has emerged as a leading candidate for CMOS-compatible low-voltage memory, combining strong spontaneous…
Decoupling structural and bonding effects on ferroelectric switching in ScAlN via molecular dynamics under an applied electric field
Ryotaro Sahashi, Po-Yen Chen, Teruyasu Mizoguchi
ScxAl1-xN has emerged as a promising wurtzite-type ferroelectric material, where increasing the Sc composition reduces both the coercive field (Ec) and remanent polarization (Pr).…
Achieving Robust Extrapolation in Materials Property Prediction via Decoupled Transfer Learning
Tasuku Sugiura, Teruyasu Mizoguchi
Machine learning has revolutionized materials property prediction, yet fails catastrophically when extrapolating beyond training distributions-precisely the capability required for…
Finite-size effects and energy alignment in molecular XANES under periodic boundary conditions: A systematic comparison of core-hole treatments
Yu Fujikata, Yasuji Muramatsu, Teruyasu Mizoguchi
X-ray absorption near-edge structure (XANES) provides element-specific insight into local electronic and structural environments, but quantitative interpretation of molecular XANES…
Generative Inverse Estimation of 3D Atomic Coordination from Near-Edge Spectra via Equivariant Diffusion Models
Ren Okubo, Yu Fujikata, Izumi Takahara +1
Extracting 3D atomic coordinates from spectroscopic data is a longstanding inverse problem. We present an equivariant diffusion model that generates site-specific 3D structures dir…
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang +18
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-iso…
Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential
Po-Yen Chen, Teruyasu Mizoguchi
Ferroelectric materials such as BaTiO3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of various memory, actuator, and sen…
Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
Pol Benítez, Cibrán López, Edgardo Saucedo +2
Machine learning (ML) methods have become powerful tools for predicting material properties with near first-principles accuracy and vastly reduced computational cost. However, the…
Electric Field-Induced Phase Transitions and Hysteresis in Ferroelectric HfO2 Captured with Machine Learning Potential
Po-Yen Chen, Teruyasu Mizoguchi
Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its ap…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…
Accelerated Inorganic Materials Design with Generative AI Agents
Izumi Takahara, Teruyasu Mizoguchi, Bang Liu
Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computational methods often struggle to efficient…
Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
Izumi Takahara, Kiyou Shibata, Teruyasu Mizoguchi
Recent advances in deep learning have enabled the generation of realistic data by training generative models on large datasets of text, images, and audio. While these models have d…
Crystal orbital overlap population based on all-electron ab initio simulation with numeric atom-centered orbitals and its application to chemical-bonding analysis in Li-intercalated layered materials
Izumi Takahara, Kiyou Shibata, Teruyasu Mizoguchi
Crystal Orbital Overlap Population (COOP) is one of the effective tools for chemical-bonding analysis, and thus it has been utilized in the materials development and characterizati…