arXiv subjects

Hua Guo

Publications and source records attributed to Hua Guo.

At least 19 recordsLinked to original sources

arXiv2026

Mode-Specific Dynamics of $\text{CO}_2$ Hydrogenation on Copper: The Hidden Role of Molecular Rotation

Catalytic hydrogenation of $\text{CO}_2$ to formate on copper is a key elementary step for $\text{CO}_2$ utilization. Previous experimental and theoretical studies suggested an Eley-Rideal mechanism for this reaction, promoted by bending vibrational excitation, yet direct state resolved evidence remains lacking. Here, we present first-principles dynamical predictions for $\text{CO}_2$ hydrogenation on Cu(111) based on an accurate full-dimensional neural network potential energy surface. Our calculations near-quantitatively reproduce the measured reaction probabilities, including their nozzle-temperature and incidence-energy dependence. Our state-resolved results indicate that while vibrational excitation of the bending mode enhances reactivity, it alone cannot account for the observed reactivity increase with nozzle temperature. Instead, rotational excitation plays a dominant role, mainly attributable to the significant change in anisotropy of the molecular polar orientation as $\text{CO}_2$ accesses the transition state. This mode-specific insight reinforces the hidden role of rotation in surface reactivity, opening new avenues for state-selective control of $\text{CO}_2$ hydrogenation on heterogenous catalysts.

physics.chem-ph↗

arXiv2026

Unexpected Collisional Rotational Excitation via Long-Range Capture and Orbiting

Collisional rotational excitation is a fundamental process in many gaseous environments. The textbook hard-sphere model stipulates that high rotational excitation results from head-on collisions, leading primarily to backward scattering, whereas long-range glancing collisions in the forward direction are inefficient for rotational energy transfer. Here, we report rotational state resolved product imaging for a system with strong attractive interaction, the charge-transfer collision between spin-orbit selected Ar+(2P3/2) ions and para/ortho-H2 molecules. Surprisingly, the H2+ products are rotationally excited and dominated by forward scattering, in sharp contrast to conventional wisdom. Quantum dynamical calculations on a first-principles diabatic potential energy matrix reproduce the observations. Trajectory surface hopping analysis further reveals that rotational excitation occurs mostly with large impact parameters, and the captured complex undergoes orbiting motion owing to the strong attractive interaction between the two collision partners before they break up. This novel mechanism should be general for collisional systems featuring strong attractive interactions, which undermine the hard-sphere assumption.

physics.chem-ph↗

arXiv2026

CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout

Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring fully sampled data for training. These methods typically partition the acquired data into two disjoint subsets to construct input-target pairs for optimizing the reconstruction network. However, existing approaches perform this partition exclusively within the spatial frequency (k-space) domain, leaving the coil dimension unexplored. To enforce full exploitation of signal correlation across receiver coils, we propose CoilDrop-MRI, which applies coil-wise dropout to the input and uses the dropped data as training targets in a self-supervised framework. This method is integrated into unrolled architectures in both image-domain (SENSE) and k-space (SPIRiT) formulations. We further demonstrate its versatility by extending CoilDrop-MRI to multi-shot, phase-corrected diffusion MRI (dMRI) reconstruction. CoilDrop-MRI is extensively validated on multi-site, multi-field-strength (0.3T, 0.55T, and 3T), and multi-modality (T1-weighted, T2-weighted, T2-FLAIR, and dMRI) datasets and consistently outperforms state-of-the-art self-supervised methods, achieving quality comparable to supervised reconstruction methods without requiring fully sampled reference training data. Moreover, CoilDrop-MRI exhibits strong data efficiency and robust generalization across imaging conditions, establishing it as a practical and versatile framework for self-supervised parallel MRI reconstruction.

cs.CV↗

arXiv2025

Real-Time Coupled Electron-Nuclear Dynamics of Chemical Bond Formation: Hydrogen Scattering from a Semiconductor Surface

A first-principles coupled electron-nuclear dynamics simulation based on real-time, time-dependent density functional theory and Ehrenfest dynamics quantitatively repro-duces bimodal translational energy loss and angular distributions observed in experiment for hydrogen atom scattering from Ge(111)-c(2*8). The theory elucidates a site-selective mechanism of electronically nonadiabatic energy transfer associated with the formation of different Ge-H bonds. When a hydrogen atom approaches a Ge rest-atom, it is strongly accelerated toward the potential minimum forming a transient Ge-H bond and then re-flected by the repulsive wall. This transient bond formation triggers an ultrafast electron transfer event from the rest-atom to an adjacent Ge-adatom, involving several crossings between valence and conduction bands of the substrate. Electronic equilibration is impos-sible within such a short time (Born-Oppenheimer failure) allowing the H-atom kinetic energy to be converted to inter-band electronic excitation of the substrate. H-atom colli-sions at other Ge atoms also form a transient bond but exhibit no electronic excitation, resulting in distinctly less efficient energy loss in scattered H-atoms. The nucle-ar-to-electronic energy transfer observed in this system reflects the electronic dynamics of covalent bond formation at a semiconductor surface, a mechanism that is quite distinct from previously identified nonadiabatic energy transfer mechanisms at metal surfaces mediated by electronic friction or transient negative ions.

physics.chem-ph↗

arXiv2025

Sedeve-Kit, a Specification-Driven Development Framework for Building Distributed Systems

Developing distributed systems presents significant challenges, primarily due to the complexity introduced by non-deterministic concurrency and faults. To address these, we propose a specification-driven development framework. Our method encompasses three key stages. The first stage defines system specifications and invariants using TLA${^+}$. It allows us to perform model checking on the algorithm's correctness and generate test cases for subsequent development phases. In the second stage, based on the established specifications, we write code to ensure consistency and accuracy in the implementation. Finally, after completing the coding process, we rigorously test the system using the test cases generated in the initial stage. This process ensures system quality by maintaining a strong connection between the abstract design and the concrete implementation through continuous verification.

cs.SE↗

arXiv2025

Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials

Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP models still incur substantial computational and memory needs due to their expensive tensor product operations over edge space, significantly limiting their applicability in large-scale or long-time simulations. In this work, we propose a node-equivariant MP (NEMP) framework that performs equivariant operations between the central node and a virtual summed node encoding structure information of its neighbors. Crucially, NEMP maintains comparable or even superior accuracy across diverse test systems-including molecules, extended systems, and universal potential benchmarks-while achieving 1-2 orders of magnitude reduction in memory and computational costs compared to edge equivariant MP models. In fact, NEMP reaches computational efficiency comparable to that of local descriptor-based models, and enabling previously inaccessible large-scale simulations.

physics.chem-ph↗

arXiv2025

Multishot Dual Polarity GRAPPA: Robust Nyquist Ghost Correction for multishot EPI

Purpose: This work aims to develop a robust Nyquist ghost correction method for multishot echo-planar imaging (EPI). The method helps correct challenging Nyquist ghosts, particularly on scanners with high-performance gradients or ultra-high fields. Methods: A method for multishot EPI ghost correction, called multishot dual-polarity GRAPPA (msDPG), is developed by extending the DPG concept to multishot readouts. msDPG employs tailored DPG kernels to address high-order phase differences between two EPI readout polarities, which cannot be fully addressed using linear phase correction (LPC). Advanced regularizers can be readily employed with the proposed msDPG for physiologic inter-shot phase variation correction during reconstruction. Additionally, a calibration refinement method is proposed to improve the quality of the DPG calibration data and enhance reconstruction performance. Results: Phantom and in vivo experiments on scanners with high-performance gradients and ultra-high fields demonstrated that msDPG achieved superior ghost correction performance than LPC, reducing the ghost-to-signal ratio (GSR) by over 50%. Compared to conventional DPG, msDPG provided images with lower noise amplification, particularly for acquisitions with large in-plane acceleration. Consequently, high-fidelity, submillimeter diffusion images were obtained using msDPG with regularized reconstruction. Conclusion: The proposed msDPG provides a robust Nyquist ghost correction method for multishot EPI, enabling submillimeter imaging with improved fidelity.

physics.med-ph↗

arXiv2025

Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images

Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immuno-histochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC.

q-bio.QM↗

arXiv2025

Extending ring polymer molecular dynamics rate theory to reactions with non-separable reactants

The ring polymer molecular dynamics (RPMD) rate theory is an efficient and accurate method for estimating rate coefficients of chemical reactions affected by nuclear quantum effects. The commonly used RPMD treatment of gas-phase bimolecular reactions adopts two dividing surfaces, one at the transition state and another in the reactant asymptote, where partition functions of separated reactants can be readily obtained. With some exceptions, however, this strategy is difficult to implement for processes on surfaces or in liquids, because the reactants are often strongly coupled with the extended medium (surface or solvent) and thus non-separable. Under such circumstances, the RPMD rate theory with a single dividing surface (SDS) is better suited. However, most of its implementations adopted Cartesian forms of the reaction coordinate, which, in many cases, are not ideal for describing complex reactions. Here, we present a SDS-based RPMD implementation, which are able to tackle the aforementioned challenges. This approach is demonstrated in four representative reactions, including the gas phase H + H2 exchange reaction, gas phase CH3NC isomerization, H recombinative desorption from Pt(111), and NO desorption from Pd(111). This implementation, which is applicable to both uni- and bi-molecular reactions, offers a unified treatment of gas-phase and surface reaction rate calculations on the same footing.

physics.chem-ph↗

arXiv2025

First Principles Reactive Flux Theory for Surface Reactions: Multiple Channels and Recrossing Dynamics

Heterogenous reactions typically consist of multiple elementary steps and their rate coefficients are of fundamental importance in elucidating the mechanisms and micro-kinetics of these processes. Transition-state theory (TST) for calculating surface reaction rate coefficients often relies solely on the harmonic approximation of adsorbent vibrations and neglects recrossing dynamics. Here, we combine, for the first time, an efficient metadynamics enhanced sampling method with a more general reactive flux approach to calculate rate coefficients of surface reactions of any order and/or with multiple reaction coordinates, overcoming these limitations of TST. We apply this approach to a textbook surface reaction, CO oxidation on Pt(111), for which rate constants have been precisely measured, using a full-dimensional neural network potential energy surface constructed from first-principles data. An accurate multi-dimensional free-energy surface is obtained by incorporating three collective variables, yielding rate coefficients for both CO oxidation and the competing CO desorption that are in good agreement with experimental data. Interestingly, our results reveal significant dynamic recrossing in both channels, which however arises from distinct physical mechanisms. This approach represents an accurate and general framework for calculating rate coefficients of elementary surface processes from first-principles, which is vital for developing predictive kinetic models for heterogenous catalysis.

physics.chem-ph↗

arXiv2025

Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI

Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, limiting their effectiveness in capturing detailed microstructural features. Furthermore, traditional pixel-wise loss functions struggle to recover intricate image details essential for high-resolution reconstruction. To address these challenges, we propose SARL-dMRI, a novel Spatial-Angular Representation Learning framework for high-fidelity, continuous super-resolution in dMRI. SARL-dMRI explores implicit neural representations and spherical harmonics to model continuous spatial and angular representations, simultaneously enhancing both spatial and angular resolution while improving microstructural parameter estimation accuracy. To further preserve image fidelity, a data-fidelity module and wavelet-based frequency loss are introduced, ensuring the super-resolved images remain consistent with the original input and retain fine details. Extensive experiments demonstrate that, compared to five other state-of-the-art methods, our method significantly enhances dMRI data resolution, improves the accuracy of microstructural parameter estimation, and provides better generalization capabilities. It maintains stable performance even under a 45$\times$ downsampling factor.

eess.IV↗

arXiv2024

Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities.

eess.IV↗

arXiv2024

Artifact Correction in Magnetic Resonance Temperature Imaging for Laser Interstitial Thermotherapy with Multi-echo Acquisitions

In MRI-guided laser interstitial thermotherapy (MRgLITT), a signal void sometimes appears at the heating center of the measured temperature map. In neurosurgical MRgLITT treatments, cerebrospinal fluid pulsation (CSF), which may lead to temperature artifacts, also needs to be carefully managed. We find that signal loss in MR magnitude images can be one distinct contributor to the temperature imaging signal void. Therefore, this study aims to investigate this finding and more importantly. Also, this study intends to improve measurement accuracy by correcting CSF-induced temperature errors and employing a more reliable phase unwrapping algorithm. A gradient echo sequence with certain TE values for temperature imaging is used to quantify T2* variations during MRgLITT and to investigate the development of signal voids throughout the treatment. Informed by these findings, a multi-echo GRE sequence with appropriate TE coverage is employed. A multi-echo-based correction algorithm is developed to address the signal loss-induced temperature errors. A new phase unwrapping method and a new CSF pulsation correction approach are developed for multi-echo signal processing. The temperature imaging method is evaluated by gel phantom, ex-vivo, and in-vivo LITT heating experiments. T2* shortening during heating can be one important cause of the temperate imaging signal voids and this demands the multi-echo acquisition with varied TE values. The proposed multi-echo-based method can effectively correct signal loss-induced temperature errors and raise temperature estimation precision. The multi-echo thermometry in the in-vivo experiments shows smoother hotspot boundaries, fewer artifacts, and improved thermometry reliability. In the in-vivo experiments, the ablation areas estimated from the multi-echo thermometry also show satisfactory agreement with those determined from post-ablation MR imaging.

physics.med-ph↗

arXiv2024

Inelastic Triatom-Atom Quantum Close-Coupling Dynamics in Full Dimensionality: all rovibrational mode quenching of water due to H impact on a six-dimensional potential energy surface

The rovibrational level populations, and subsequent emission in various astrophysical environments, is driven by inelastic collision processes. The available rovibrational rate coefficients for water have been calculated using a number of approximations. We present a numerically exact calculation for the rovibrational quenching for all water vibrational modes due to collisions with atomic hydrogen. The scattering theory implements a quantum close-coupling (CC) method on a high level ab initio six-dimensional (6D) potential energy surface (PES). Total rovibrational quenching cross sections for excited bending levels were compared with earlier results on a 4D PES with the rigid-bender close-coupling (RBCC) approximation. General agreement between 6D-CC and 4D-RBCC calculations are found, but differences are evident including the energy and amplitude of low-energy orbiting resonances. Quenching cross sections from the symmetric and asymmetric stretch modes are provided for the first time. The current 6D-CC calculation provides accurate inelastic data needed for astrophysical modeling.

astro-ph.GA↗

arXiv2024

Schr\"{o}dingerNet: A Universal Neural Network Solver for The Schr\"{o}dinger Equation

Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schr\"{o}dinger equation (SE) by integrating various neural network (NN)-based wavefunction ansatzes with variational Monte Carlo methods. Nevertheless, such NN-based methods are all based on the Born-Oppenheimer approximation (BOA) and require computationally expensive training for each nuclear configuration. In this work, we propose a novel NN architecture, Schr\"{o}dingerNet, to solve the full electronic-nuclear SE by defining a loss function designed to equalize local energies across the system. This approach is based on a translationally, rotationally and permutationally symmetry-adapted total wavefunction ansatz that includes both nuclear and electronic coordinates. This strategy not only allows for an efficient and accurate generation of a continuous potential energy surface at any geometry within the well-sampled nuclear configuration space, but also incorporates non-BOA corrections, through a single training process. Comparison with benchmarks of atomic and small molecular systems demonstrates its accuracy and efficiency.

physics.chem-ph↗

arXiv2024

Comprehensive characterization of tumor therapeutic response with simultaneous mapping cell size, density, and transcytolemmal water exchange

Early assessment of tumor therapeutic response is an important topic in precision medicine to optimize personalized treatment regimens and reduce unnecessary toxicity, cost, and delay. Although diffusion MRI (dMRI) has shown potential to address this need, its predictive accuracy is limited, likely due to its unspecific sensitivity to overall pathological changes. In this work, we propose a new quantitative dMRI-based method dubbed EXCHANGE (MRI of water Exchange, Confined and Hindered diffusion under Arbitrary Gradient waveform Encodings) for simultaneous mapping of cell size, cell density, and transcytolemmal water exchange. Such rich microstructural information comprehensively evaluates tumor pathologies at the cellular level. Validations using numerical simulations and in vitro cell experiments confirmed that the EXCHANGE method can accurately estimate mean cell size, density, and water exchange rate constants. The results from in vivo animal experiments show the potential of EXCHANGE for monitoring tumor treatment response. Finally, the EXCHANGE method was implemented in breast cancer patients with neoadjuvant chemotherapy, demonstrating its feasibility in assessing tumor therapeutic response in clinics. In summary, a new, quantitative dMRI-based EXCHANGE method was proposed to comprehensively characterize tumor microstructural properties at the cellular level, suggesting a unique means to monitor tumor treatment response in clinical practice.

physics.med-ph↗

arXiv2024

Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priors

Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited slices. In this study, we introduce ROGER, a robust SMS MRI reconstruction method based on deep generative priors. Utilizing denoising diffusion probabilistic models (DDPM), ROGER begins with Gaussian noise and gradually recovers individual slices through reverse diffusion iterations while enforcing data consistency from measured k-space data within the readout concatenation framework. The posterior sampling procedure is designed such that the DDPM training can be performed on single-slice images without requiring modifications for SMS tasks. Additionally, our method incorporates a low-frequency enhancement (LFE) module to address the practical issue that SMS-accelerated fast spin echo (FSE) and echo planar imaging (EPI) sequences cannot easily embed fully-sampled autocalibration signals. Extensive experiments on both retrospectively and prospectively accelerated datasets demonstrate that ROGER consistently outperforms existing methods, enhancing both anatomical and functional imaging with strong out-of-distribution generalization. The source code and sample data for ROGER are available at https://github.com/Solor-pikachu/ROGER.

eess.IV↗

arXiv2024

Simultaneous Multi-Slice Diffusion Imaging using Navigator-free Multishot Spiral Acquisition

Purpose: This work aims to raise a novel design for navigator-free multiband (MB) multishot uniform-density spiral (UDS) acquisition and reconstruction, and to demonstrate its utility for high-efficiency, high-resolution diffusion imaging. Theory and Methods: Our design focuses on the acquisition and reconstruction of navigator-free MB multishot UDS diffusion imaging. For acquisition, radiofrequency (RF) pulse encoding was employed to achieve Controlled Aliasing in Parallel Imaging (CAIPI) in MB imaging. For reconstruction, a new algorithm named slice-POCS-enhanced Inherent Correction of phase Errors (slice-POCS-ICE) was proposed to simultaneously estimate diffusion-weighted images and inter-shot phase variations for each slice. The efficacy of the proposed methods was evaluated in both numerical simulation and in vivo experiments. Results: In both numerical simulation and in vivo experiments, slice-POCS-ICE estimated phase variations more precisely and provided results with better image quality than other methods. The inter-shot phase variations and MB slice aliasing artifacts were simultaneously resolved using the proposed slice-POCS-ICE algorithm. Conclusion: The proposed navigator-free MB multishot UDS acquisition and reconstruction method is an effective solution for high-efficiency, high-resolution diffusion imaging.

physics.med-ph↗