arXiv subjects

Bin Xing

Publications and source records attributed to Bin Xing.

8 recordsLinked to original sources

arXiv2026

Fragmenting Diffusion Pathways Confers Extraordinary Radiation Resistance in Refractory Multicomponent Alloys

The accumulation and growth of vacancy clusters under irradiation is a pivotal degradation mode for structural materials in extreme environments. Even tungsten undergoes rapid defect coarsening compromising its integrity. Here we show a tungsten multicomponent alloy that effectively fragments the vacancy diffusion network, kinetically trapping defects within localized domains. This effect originates from a broad spectrum of migration barriers and substantial vacancy-jump heterogeneity, which drives the interconnectivity of diffusion paths below the percolation threshold. Starving clusters of the necessary vacancy supply, irradiation experiments and atomic-scale defect characterizations confirm negligible defect growth as radiation doses increase by four orders of magnitude. These results provide a fundamental paradigm for percolation-engineered kinetics, offering a predictive pathway for tailoring defect diffusion and discovering inherently radiation-tolerant materials.

cond-mat.mtrl-sci↗

arXiv2025

Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought

As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mamba's long-sequence processing efficiency with Transformer's superior contextual understanding. Hunyuan-TurboS features an adaptive long-short chain-of-thought (CoT) mechanism, dynamically switching between rapid responses for simple queries and deep "thinking" modes for complex problems, optimizing computational resources. Architecturally, this 56B activated (560B total) parameter model employs 128 layers (Mamba2, Attention, FFN) with an innovative AMF/MF block pattern. Faster Mamba2 ensures linear complexity, Grouped-Query Attention minimizes KV cache, and FFNs use an MoE structure. Pre-trained on 16T high-quality tokens, it supports a 256K context length and is the first industry-deployed large-scale Mamba model. Our comprehensive post-training strategy enhances capabilities via Supervised Fine-Tuning (3M instructions), a novel Adaptive Long-short CoT Fusion method, Multi-round Deliberation Learning for iterative improvement, and a two-stage Large-scale Reinforcement Learning process targeting STEM and general instruction-following. Evaluations show strong performance: overall top 7 rank on LMSYS Chatbot Arena with a score of 1356, outperforming leading models like Gemini-2.0-Flash-001 (1352) and o4-mini-2025-04-16 (1345). TurboS also achieves an average of 77.9% across 23 automated benchmarks. Hunyuan-TurboS balances high performance and efficiency, offering substantial capabilities at lower inference costs than many reasoning models, establishing a new paradigm for efficient large-scale pre-trained models.

cs.CL↗

arXiv2024

Deep Learning of Mean First Passage Time Scape: Chemical Short-Range Order and Kinetics of Diffusive Relaxation

Processes slow compared to atomic vibrations pose significant challenges in atomistic simulations, particularly for phenomena such as diffusive relaxations and phase transitions, where repeated crossings and the shear number of thermally activated transitions make direct numerical simulations impossible. We present a computational framework that captures atomic-scale diffusive relaxation over extended timescales by learning the mean first passage time (MFPT) with a deep neural network. The model is trained via a self-consistent recursive formulation based on the Markovian assumption, relying solely on local residence times and transition probabilities between neighboring states. Furthermore, we leverage deep reinforcement learning (DRL)-accelerated atomistic simulations to expedite the identification of thermodynamic equilibrium and the generation of accurate physical transition probabilities. Applied to vacancy-mediated chemical short-range order (SRO) evolution in equiatomic CrCoNi, our method uncovers disorder-to-order transition timescales in quantitative agreement with experimental measurements. By bridging the gap between simulation and experiment, our approach extends atomistic modeling to previously inaccessible timescales and offers a predictive tool for navigating process-structure-property relationships.

cond-mat.mtrl-sci↗

arXiv2024

Divergent Evolution of Slip Banding in Alloys

Metallic materials under high stress often exhibit deformation localization, manifesting as slip banding. Over seven decades ago, Frank and Read introduced the well-known model of dislocation multiplication at a source, explaining slip band formation. Here, we reveal two distinct types of slip bands (confined and extended) in alloys through multi-scale testing and modeling from microscopic to atomic scales. The confined slip band, characterized by a thin glide zone, arises from the conventional process of repetitive full dislocation emissions at Frank-Read source. Contrary to the classical model, the extended band stems from slip-induced deactivation of dislocation sources, followed by consequent generation of new sources on adjacent planes, leading to rapid band thickening. Our findings provide critical insights into atomic-scale collective dislocation motion and microscopic deformation instability in advanced structural materials, marking a pivotal advancement in our fundamental understanding of deformation dynamics.

cond-mat.mtrl-sci↗

arXiv2024

A simple model for short-range ordering kinetics in multi-principal element alloys

Short-range ordering (SRO) in multi-principal element alloys influences material properties such as strength and corrosion. While some degree of SRO is expected at equilibrium, predicting the kinetics of its formation is challenging. We present a simplified isothermal concentration-wave (CW) model to estimate an effective relaxation time of SRO formation. Estimates from the CW model agree to within a factor of five with relaxation times obtained from kinetic Monte Carlo (kMC) simulations when above the highest ordering instability temperature. The advantage of the CW model is that it only requires mobility and thermodynamic parameters, which are readily obtained from alloy mobility databases and Metropolis Monte Carlo simulations, respectively. The simple parameterization of the CW model and its analytical nature make it an attractive tool for the design of processing conditions to promote or suppress SRO in multicomponent alloys.

cond-mat.mtrl-sci↗

arXiv2024

Atomic-scale tunable phonon transport at tailored grain boundaries and Their Impact on Thermal Conductivity

Grain boundaries (GBs) strongly influence thermal transport in crystalline solids by disrupting lattice periodicity and scattering phonons. Due to the atomic-level disorder and structural complexity, a fundamental understanding of how specific GB geometries regulate nanoscale phonon behavior and macroscopic thermal conductivity has remained elusive. Here, using emerging atomic-resolution vibrational spectroscopy, we directly correlate GB structure, defect-specific vibrational states, and thermal transport in bicrystal strontium titanate with controlled tilt and twist angles. The phonon characterizations and thermal conductivity data reveal two distinct regimes, where low-angle tilt GBs (2deg, 6deg, 10deg) substantially modulate phonon populations and mode frequencies, resulting in pronounced changes in thermal conductivity, whereas high-angle tilt GBs (22deg, 36deg) exhibit weak conductivity variation due to saturated structural disorder and scattering. In contrast, twist GBs introduce periodic defect motifs that only locally tailor phonon transport. Our results suggest tilt and twist angles as complementary knobs for coarse and fine control of phonon propagation and cross-GB thermal transport, providing a predictive framework for thermal engineering in materials.

cond-mat.mes-hall↗

arXiv2023

Neural Network Kinetics for Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. Local chemical complexity in compositionally complex alloys poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. Using this method, we study the temperature-dependent local chemical ordering in a refractory Nb-Mo-Ta alloy and reveal a critical temperature at which the B2 order reaches a maximum. Our atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

cond-mat.dis-nn↗

arXiv2022

Convolutional neural networks enable high-fidelity prediction of path-dependent diffusion barrier spectra in multi-principal element alloys

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. However, how to efficiently identify optimal compositions from such a large design space for targeted properties is a grand challenge in material science. Here we developed a convolutional neural network (CNN) model that can accurately and efficiently predict path-dependent vacancy migration energy barriers, which are critical to diffusion behaviors and many high-temperature properties, of MPEAs at any compositions and with different chemical short-range orders within a given alloy system. The success of the CNN model makes it promising for developing a database of diffusion barriers for different MPEA systems, which would accelerate alloy screening for the discovery of new compositions with desirable properties. Besides, the length scale of local configurations relevant to migration energy barriers is uncovered, and the implications of this success to other aspects of materials science are discussed.

cond-mat.mtrl-sci↗