arXiv subjects

Zhenbin Wang

Publications and source records attributed to Zhenbin Wang.

11 recordsLinked to original sources

arXiv2026

Predicting Novel Stable Materials for Experimental Synthesis

Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning interatomic potentials, and SCAN-based thermodynamic refinement. Applying this protocol to the 894 stable materials previously reported in Sci. Data 9, 302 (2022), we first curate 603 unique structures, of which only 298 remain thermodynamically stable on the complete PBE phase diagrams, demonstrating the critical role of competing phases in stability assessment. Dynamical screening then identifies 166 materials stable under both harmonic-phonon and finite-temperature molecular dynamics criteria, and SCAN phase diagrams further narrow this set to 109. Finally, by combining decomposition enthalpy with chemical-space completeness, we prioritize 25 candidates as high-confidence targets for experimental synthesis. This work provides a practical protocol for translating stability predictions into experimentally actionable synthesis targets, closing a key gap in machine-learning-driven materials discovery.

cond-mat.mtrl-sci↗

arXiv2026

Melt-Quench Failures and Practical Solutions for Universal Machine-Learning Interatomic Potentials in Amorphous Structure Generation

Generating experimentally relevant amorphous structures via melt-quench molecular dynamics is prohibitively expensive at the first-principles level. Universal machine-learning interatomic potentials (uMLIPs) could accelerate such simulations, but their reliability under non-equilibrium conditions remains unclear. Here, we examine eight leading uMLIPs for generating amorphous IrO2, using this electrocatalytically relevant oxide as a diagnostic case. Under the conventional melt-quench protocol, all models yield unphysically expanded structures with densities of 1-4 g/cm3, far below the ab initio molecular dynamics (AIMD) reference value of 10.04 g/cm3. Comparisons against ab initio references show that accurate energies and forces alone do not ensure stable NPT dynamics; correct energy-volume responses and pressure predictions are also essential. We identify two practical remedies: pressure-targeted fine-tuning and a revised NVT-quench/NPT-equilibration protocol that avoids unphysical volume expansion without additional ab initio training data. Both recover IrO2 densities and local structures consistent with AIMD. Across 30 chemically diverse materials, the volume-expansion failure proves general, and the revised protocol substantially improves density predictions, reducing the AIMD-referenced MAE from 2.46 to 0.35 g/cm3. This work establishes practical validation criteria and simulation strategies for robust uMLIP-driven amorphous structure generation.

cond-mat.mtrl-sci↗

arXiv2026

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction

Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However, their effectiveness in identifying new, complex materials remains uncertain. Here, we systematically assess the capability of a uMLIP (i.e.,M3GNet) to accelerate CSP in quaternary oxides. Through extensive exploration of the Sr-Li-Al-O and Ba-Y-Al-O systems, we show that uMLIP can rediscover experimentally known materials absent from its training set and identify seven new thermodynamically and dynamically stable compounds. These include a new polymorph of Sr2LiAlO4 (P3221) and a new disordered phase, Sr2Li4Al2O7 (P1_bar). Furthermore, our results show stability predictions based on the semilocal PBE functional require cross-validation with higher-level methods, such as SCAN and RPA, to ensure reliability. While uMLIPs substantially reduce the computational cost of CSP, the primary bottleneck has shifted to the efficiency of search algorithms in navigating complex structural spaces. This work highlights both the promise and current limitations of uMLIP-driven CSP in the discovery of new materials.

cond-mat.mtrl-sci↗

arXiv2026

Are Universal Potentials Ready for Alkali-Ion Battery Kinetics?

Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in capturing these high-energy landscapes remains uncertain. Here, we systematically benchmark state-of-the-art uMLIPs, including M3GNet, CHGNet, MACE, SevenNet, GRACE, and Orb, against DFT baselines for cathodes and solid electrolytes. We find that the Orb-v3 family excels in static migration barrier predictions (MAE $\approx$ 75--111 meV), driven primarily by architectural refinements. Conversely, for dynamic transport, the GRACE model trained on the OMat24 dataset demonstrates superior fidelity in reproducing ion diffusivities and structural correlations. Our results reveal that while architectural sophistication (e.g., equivariance) is beneficial, the inclusion of high-temperature, non-equilibrium training data is the dominant driver of kinetic accuracy. These findings establish that modern uMLIPs are sufficiently robust to serve as zero-shot surrogates for high-throughput kinetic screening of next-generation energy storage materials.

cond-mat.mtrl-sci↗

arXiv2024

Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation

Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, and simulation. Current video diffusion models typically build on image diffusion architecture by incorporating temporal operations (such as 3D convolution and temporal attention). Although this approach is effective, its oversimplification limits spatio-temporal performance and consumes substantial computational resources. To counter this, we propose Medical Simulation Video Generator (MedSora), which incorporates three key elements: i) a video diffusion framework integrates the advantages of attention and Mamba, balancing low computational load with high-quality video generation, ii) an optical flow representation alignment method that implicitly enhances attention to inter-frame pixels, and iii) a video variational autoencoder (VAE) with frequency compensation addresses the information loss of medical features that occurs when transforming pixel space into latent features and then back to pixel frames. Extensive experiments and applications demonstrate that MedSora exhibits superior visual quality in generating medical videos, outperforming the most advanced baseline methods. Further results and code are available at https://wongzbb.github.io/MedSora

cs.CV↗

arXiv2024

Soft Masked Mamba Diffusion Model for CT to MRI Conversion

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the predominant modalities utilized in the field of medical imaging. Although MRI capture the complexity of anatomical structures with greater detail than CT, it entails a higher financial costs and requires longer image acquisition times. In this study, we aim to train latent diffusion model for CT to MRI conversion, replacing the commonly-used U-Net or Transformer backbone with a State-Space Model (SSM) called Mamba that operates on latent patches. First, we noted critical oversights in the scan scheme of most Mamba-based vision methods, including inadequate attention to the spatial continuity of patch tokens and the lack of consideration for their varying importance to the target task. Secondly, extending from this insight, we introduce Diffusion Mamba (DiffMa), employing soft masked to integrate Cross-Sequence Attention into Mamba and conducting selective scan in a spiral manner. Lastly, extensive experiments demonstrate impressive performance by DiffMa in medical image generation tasks, with notable advantages in input scaling efficiency over existing benchmark models. The code and models are available at https://github.com/wongzbb/DiffMa-Diffusion-Mamba

cs.CV↗

arXiv2024

LanDA: Language-Guided Multi-Source Domain Adaptation

Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when transferring knowledge from multiple labeled source domains to an unlabeled target domain. However, existing MSDA techniques assume target domain images are available, yet overlook image-rich semantic information. Consequently, an open question is whether MSDA can be guided solely by textual cues in the absence of target domain images. By employing a multimodal model with a joint image and language embedding space, we propose a novel language-guided MSDA approach, termed LanDA, based on optimal transfer theory, which facilitates the transfer of multiple source domains to a new target domain, requiring only a textual description of the target domain without needing even a single target domain image, while retaining task-relevant information. We present extensive experiments across different transfer scenarios using a suite of relevant benchmarks, demonstrating that LanDA outperforms standard fine-tuning and ensemble approaches in both target and source domains.

cs.CV↗

arXiv2023

Operando Insights on the Degradation Mechanisms of Rhenium-doped and Undoped Molybdenum Disulfide Nanocatalysts for Electrolyzer Applications

MoS2 nanostructures are promising catalysts for proton-exchange-membrane (PEM) electrolyzers to replace expensive noble metals. Their broadscale application demands high activity for the hydrogen evolution reaction (HER) as well as robust durability. Doping is commonly applied to enhance the HER activity of MoS2-based nanocatalysts, but the effect of dopants in the electrochemical and structural stability is yet to be discussed. Herein, we correlate operando electrochemical measurements to the structural evolution of the materials down to the nanometric scale by identical location electron microscopy and spectroscopy. The range of stable operation for MoS2 nanocatalysts with and without rhenium doping is experimentally defined. The responsible degradation mechanisms at first electrolyte contact, open circuit stabilization and HER conditions are experimentally identified and confirmed with the calculated Pourbaix diagram of Re-doped MoS2. Doping MoS2-based nanocatalysts is validated as a promising strategy for the continuous improvement of high performance and durable PEM electrolyzers.

physics.app-ph↗

arXiv2020

Unified Theory of Thermal Quenching in Inorganic Phosphors

We unify two prevailing theories of thermal quenching (TQ) in rare-earth-activated inorganic phosphors - the cross-over and auto-ionization mechanisms - into a single predictive model. Crucially, we have developed computable descriptors for activator environment stability from ab initio molecular dynamics simulations to predict TQ under the cross-over mechanism, which can be augmented by a band gap calculation to account for auto-ionization. The resulting TQ model predicts the experimental TQ in 29 known phosphors to within ~ 3-8%. Finally, we have developed an efficient topological approach to rapidly screen vast chemical spaces for the discovery of novel, thermally robust phosphors.

cond-mat.mtrl-sci↗

arXiv2017

Deep Neural Networks for Accurate Predictions of Garnet Stability

Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations are the computational tool of choice to obtain energies of crystals with quantitative accuracy. Despite algorithmic and computing advances, DFT calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors - the Pauling electronegativity and ionic radii - can predict the DFT formation energies of C3A2D3O12 garnets with extremely low mean absolute errors of 7-8 meV/atom, an order of magnitude improvement over previous machine learning models and well within the limits of DFT accuracy. Further extension to mixed garnets with little loss in accuracy can be achieved using a binary encoding scheme that introduces minimal increase in descriptor dimensionality. Our results demonstrate that generalizable deep-learning models for quantitative crystal stability prediction can be built on a small set of chemically-intuitive descriptors. Such models provide the means to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties.

cond-mat.mtrl-sci↗

arXiv2016

Electronic Structure Descriptor for Discovery of Narrow-Band Red-Emitting Phosphors

Narrow-band red-emitting phosphors are a critical component in phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu2+-activated emission identified through a comparison of the electronic structure of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu2+ 4f7 bands. By incorporating this descriptor in a high throughput first principles screening of 2,259 nitride compounds, we identify five promising new nitride hosts for Eu2+-activated red-emitting phosphors that are predicted to exhibit good chemical stability, thermal quenching resistance and quantum efficiency, as well as narrow-band emission. Our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts, and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.

cond-mat.mtrl-sci↗