High-pressure crystal structure prediction (CSP) underpins advances in condensed matter physics, planetary science, and materials discovery. However, most large atomistic models are trained on near-ambient equilibrium data, which leads to reduced stress accuracy at tens to hundreds of gigapascals and limited coverage of pressure-stabilized stoichiometries and dense coordination...
Antiferromagnets (AFMs) offer unique advantages for next-generation spintronic devices, yet their discovery remains hindered by the vast chemical space and the lack of symmetry-aware design strategies. Here we present a symmetry-informed generative framework that explicitly encodes crystal symmetry into the structural representation, enabling targeted exploration of high-symmetry...
Irradiation-induced void swelling is a major cause of microstructural degradation in structural materials for nuclear applications. Although Ni-based concentrated solid solution alloys (CSAs) exhibit superior swelling resistance, the vacancy-mediated mechanisms by which chemical complexity alters long-term evolution and suppresses the emergence of void-like structures remain...
Spatially-resolved electron energy loss spectroscopy in scanning transmission electron microscopy for materials characterization provides unparalleled access to nanoscale mapping of physical and chemical properties. In particular, the low-loss energy regime contains key details for understanding the optical (e.g., plasmons), electronic (e.g., band gaps), and structural...
Piezoresponse force microscopy (PFM), a lock-in-based mode of atomic force microscopy (AFM), enables sensitive ferroelectric domain imaging. However, fast imaging for either massive measurements or studying fast dynamic behavior remains challenging because it suffers from a fundamental trade-off between fast acquisition and high signal quality. To address this challenge, we...
Atomic Force Microscopy (AFM) is a cornerstone of nanometrology but is constrained by slow acquisition and processing speeds. To address this bottleneck, we introduce AFM-net, a machine learning framework that simultaneously replaces conventional post-processing pipelines and enables high-speed imaging by reconstructing high-fidelity data from fast, low-quality scans. AFM-net...
Computational searches for protective coatings stabilizing electrode-electrolyte interfaces in solid-state batteries often rely on the Li-ion migration barrier as a descriptor of ionic conductivity. While attractive for high-throughput screening, this approach can be misleading unless the equilibrium concentration of defects governing ionic transport is explicitly considered...
Understanding how sulphur-bearing molecules interact with catalytic grain analogues is relevant to the long-standing problem of sulphur depletion in dense interstellar environments. Here, periodic density functional theory, climbing-image nudged elastic band calculations, and kinetic modelling are used to investigate CS hydrogenation by a single-atom Fe⁰ site on amorphous silica...
Magnetic exchange interactions govern the macroscopic magnetic behavior of solids and underpin both fundamental spin phenomena and emerging technologies. The accurate and efficient determination of these interactions is therefore critical for predictive modeling of magnetic materials. Here we present a systematic first-principles comparison of three widely used approaches—the...
Mechanical metamaterials derive their exceptional properties from architected microstructures, yet inverse design for tailored performance remains computationally intensive and unintuitive. Here, we introduce TrussGPT, a large language model (LLM)-enabled generative framework that integrates natural language understanding with structural generation for automated inverse design of...
X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under...
Controlling phonon-mediated heat transport in multilayer nanostructures is central to thermal management and energy-conversion technologies. Yet identifying architectures exhibiting extreme lattice thermal conductivity (κl) remains challenging due to the combinatorial complexity of interface arrangements and the high computational cost of transport simulations. In superlattices...
Spin-charge interconversion is a central functionality of spintronics. Using three effective k ⋅ p models, we reveal how spin-charge interconversion is governed by the spin texture of Rashba-type bands, distinguishing systems with opposite-chirality spin-split Fermi contours (conventional Rashba bands) from those with same-chirality contours (unconventional Rashba bands). High...
Searching for ambient-pressure conventional superconductors with critical temperatures (TC) higher than 40 K is a key challenge in the field of high-temperature superconductivity, mainly due to lack of efficient and effective models for rapidly screening candidate systems. In this work, we propose a simplified model that separates the dimensionless electron-phonon coupling (EPC...
The electrocatalytic performance of FeNₓ sites embedded in multilayer graphene (FeNx/MLG) for the oxygen reduction (ORR) and oxygen evolution (OER) reactions was systematically investigated using density functional theory within an electrochemical thermodynamic framework. FeNx supported on bilayer graphene (FeNx/BLG) exhibits superior thermodynamic, electrochemical, and dynamic...
Covalent organic frameworks (COFs) are promising photocatalysts for solar hydrogen production, yet the most electronically favorable linkages, imines,hydrolyze rapidly in water, creating a stability-activity trade-off that limits practical deployment. Navigating the combinatorial design space of nodes, linkers, linkages, and functional groups to identify candidates that are...
Bayesian optimization (BO) is increasingly used for experimental design in materials science, chemistry, and biology, yet existing libraries and underlying concepts can be complex for non-machine-learning experts. To address this barrier, we introduce Honegumi, an interactive tool that simplifies creating advanced Bayesian optimization scripts. Honegumi provides a dynamic...
Topological spin textures such as skyrmions and bimerons offer rich physical functionalities and strong potential for future spin-based technologies. Identifying materials that host such emergent magnetic states, however, remains challenging due to the complex interplay between microscopic magnetic interactions and large-scale spin textures. Here, using a multiscale first...
Kohn-Sham density functional theory (KS-DFT) has found widespread application in accurate electronic structure calculations. However, it can be computationally demanding especially for large-scale simulations, motivating recent efforts toward its machine-learning (ML) acceleration. We propose a neural network self-consistent fields (NeuralSCF) framework that establishes the Kohn...
Bayesian optimization-based active learning has been widely used in materials design fields. Its core lies in the usage of Bagging or Gaussian Processes model-based Expected Improvement strategies. However, the former (BGEI) is significantly more efficient than the latter (GPEI) in the short term, which contradicts the common belief that Bayesian optimization requires long-term...
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of...
Microstructural mapping of polycrystalline metallic alloys is essential for predicting their macroscopic mechanical performance. Among existing techniques, non-destructive subsurface imaging offers a promising but technically challenging pathway for advancing the characterization of metallic materials. This study introduces a water-immersion ultrasound full waveform inversion...
The development of new materials is a key challenge in modern materials science. New materials with specific target properties have great application potential, but their generation faces multiple hurdles: data scarcity, structural feasibility, and controlled property directionality. Traditional crystal generation methods lack interpretability and fail to fully utilize existing...
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided diffusion framework to accelerate the discovery of novel superconductors. A DiffCSP foundation model is pretrained on the Alexandria Database...
As semiconductor devices approach the sub-2 nm technology node, identifying the fundamental quantum limits of contact-resistance scaling becomes imperative; however, the transition from thermionic emission to direct tunneling remains experimentally inaccessible and theoretically ill-defined. Herein, based on multi-space constrained-search density functional theory, which self...