arXiv subjects

Jiayue Han

Publications and source records attributed to Jiayue Han.

9 recordsLinked to original sources

arXiv2026

Nonadiabatic Dynamics near Multiple Light-Induced Conical Intersections under Bichromatic Driving: Quantum Wave-Packet Dynamics versus Floquet Surface Hopping

Light-induced conical intersections (LICIs) create externally tunable pathways for nonadiabatic transitions, enabling active control of molecular photophysical and photochemical processes. However, both the dynamics near multiple LICIs under bichromatic driving and the applicability of our recently developed two-mode Floquet fewest switches surface hopping (two-mode F-FSSH) method to this regime remain insufficiently understood. Here, we construct a minimal three-channel Floquet Hamiltonian supporting two LICIs for Na2 interacting with a bichromatic field. We characterize its static Floquet properties and investigate the associated nonadiabatic dynamics using numerically exact quantum wave-packet dynamics and two-mode F-FSSH. We find that the second photon energy controls the relative positions of the LICIs, whereas the second-field intensity primarily redistributes and broadens the derivative-coupling landscape around the second LICI. Consequently, electronic population transfer and molecular alignment exhibit distinct and often nonmonotonic responses to these two control parameters. Two-mode F-FSSH reliably captures the principal features of the early-time dynamics and provides a semiquantitative description of the post-transient time-averaged observables. These findings advance our understanding of LICI-mediated dynamics at both the physical and methodological levels.

physics.chem-ph↗

arXiv2026

Floquet Nonadiabatic Dynamics for Light-Matter Interactions: Recent Advances and Emerging Opportunities

Light-matter interactions provide versatile routes for probing and controlling chemical reactivity, charge transport, and material properties. Time-periodic external fields can reshape electronic states and open new dynamical pathways beyond the field-free Born-Oppenheimer (BO) picture. Floquet nonadiabatic dynamics has consequently emerged as an important framework for describing coupled electron-nuclear dynamics under periodic driving. In this Perspective, we first discuss recent developments in Floquet nonadiabatic dynamics methods for closed and open quantum systems. We then highlight how this framework provides mechanistic insights into electron transfer at molecule-metal interfaces, quantum transport in molecular junctions, carrier dynamics in crystalline solids, and multicolor Floquet engineering. Finally, we outline key conceptual and computational challenges that must be addressed to transform Floquet nonadiabatic dynamics from model-based demonstrations into predictive, first-principles simulations of realistic light-driven processes.

physics.chem-ph↗

arXiv2026

Two-Mode Floquet Fewest Switches Surface Hopping for Nonadiabatic Dynamics Driven by Two-Frequency Laser Fields

Two-frequency (two-color) laser fields provide a powerful and flexible means for steering molecular dynamics. However, quantitatively reliable and scalable theoretical tools for simulating laser-driven nonadiabatic processes under such fields remain limited. Here, we develop a two-mode Floquet fewest switches surface hopping (two-mode F-FSSH) approach for two-frequency driving within a mixed quantum-classical framework. We validate the algorithm on three driven one-dimensional two-state models: a Rabi model and two avoided-crossing scattering models. The electronic and nuclear dynamics are benchmarked against numerically exact results from split-operator calculations, showing good agreement across a broad range of field parameters and initial conditions. These results establish two-mode F-FSSH as a practical framework for simulating and designing two-frequency control protocols and motivate extensions to more realistic experimental settings.

physics.chem-ph↗

arXiv2025

Mixed Quantum-Classical Approaches to Spin Current and Polarization Dynamics in Chiral Molecular Junctions

Chiral molecular junctions offer a promising platform for realizing chiral-induced spin selectivity (CISS), where spin filtering occurs without external magnetic fields. Here, we investigate spin transport in such junctions by combining quantum master equation (QME) methods for purely electronic dynamics with surface hopping (SH) and mean-field Ehrenfest (MF) approaches to incorporate electron-phonon coupling. Our results show that transient spin polarization arises but ultimately decays to zero at long times. We find that bias voltage, molecular length, and spin-orbit coupling (SOC) strongly influence the spin current dynamics: higher bias enhances spin current but reduces polarization, while longer molecules and stronger SOC amplify transient polarization. Including electron-phonon coupling modifies current-voltage characteristics, enhancing spin currents at intermediate bias but suppressing them at high bias, while leaving the polarization dynamics largely unchanged. These findings highlight the interplay between electronic and vibrational effects in CISS and provide guidance for designing molecular spintronic devices.

cond-mat.mes-hall↗

arXiv2025

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models

Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data. However, these models suffer from the shortcut problem deeply rooted in auto-regressive prediction, causing error accumulation. The challenge becomes particularly evident for out-of-distribution data, as the pretraining performance may approach random model initialization for downstream tasks with long-term dynamics. To deal with this problem, we propose physics-informed temporal alignment (PITA), a self-supervised learning framework inspired by inverse problem solving. Specifically, PITA aligns the physical dynamics discovered at different time steps on each given PDE trajectory by integrating physics-informed constraints into the self-supervision signal. The alignment is derived from observation data without relying on known physics priors, indicating strong generalization ability to the out-of-distribution data. Extensive experiments show that PITA significantly enhances the accuracy and robustness of existing foundation models on diverse time-dependent PDE data. The code is available at https://github.com/SCAILab-USTC/PITA.

cs.LG↗

arXiv2025

Joint Range-modulator and Spot Optimization for Bragg-peak Proton FLASH Radiotherapy

Background: Ultra-high-dose-rate (UHDR) radiation therapy has demonstrated promising potential in reducing toxicity to organs-at-risk (OARs). Proton therapy is uniquely positioned to deliver UHDR by leveraging the Bragg peak in conjunction with patient-specific range modulators (PSRMs) to generate a spread-out Bragg peak (SOBP). Existing proton FLASH (pFLASH) planning typically involves (1) generating a multi-energy IMPT plan for spot weights and (2) converting it to single-energy delivery via PSRM optimization. However, the intrinsic coupling between spot weight distribution and PSRM design has not been fully investigated. Purpose: This work proposes Joint Range-Modulator and Spot Optimization (JRSO) that simultaneously optimizes the PSRM and spot weights to improve the plan quality of conformal pFLASH therapy. Methods: Unlike the conventional method, JRSO does not require a one-to-one correspondence between beam spots and PSRM pins. To achieve better plan quality, starting from an initial solution derived from a conventional IMPT plan, JRSO alternatively updates the PSRM design and spot weights. This process progressively refines both parameters while ensuring compliance with practical delivery constraints, such as the minimum monitor-unit (MMU) requirement. Results: JRSO obtained improved plan quality compared to the conventional method. For example, in a head-and-neck (HN) case, JRSO lowered the maximum target dose from 117.6% to 107.1%, improved the conformity index from 0.74 to 0.87, and decreased the region-of-interest (ROI) effective dose from 6.50 Gy to 6.10 Gy. Conclusion: A new optimization method JRSO is proposed for conformal pFLASH radiotherapy. It outperforms the conventional approach and may extend the applicability of PSRM to more complex clinical scenarios, particularly those involving misalignments between beam spots and pins.

physics.med-ph↗

arXiv2024

StringNET: Neural Network based Variational Method for Transition Pathways

Rare transition events in meta-stable systems under noisy fluctuations are crucial for many non-equilibrium physical and chemical processes. In these processes, the primary contributions to reactive flux are predominantly near the transition pathways that connect two meta-stable states. Efficient computation of these paths is essential in computational chemistry. In this work, we examine the temperature-dependent maximum flux path, the minimum energy path, and the minimum action path at zero temperature. We propose the StringNET method for training these paths using variational formulations and deep learning techniques. Unlike traditional chain-of-state methods, StringNET directly parametrizes the paths through neural network functions, utilizing the arc-length parameter as the main input. The tasks of gradient descent and re-parametrization in the string method are unified into a single framework using loss functions to train deep neural networks. More importantly, the loss function for the maximum flux path is interpreted as a softmax approximation to the numerically challenging minimax problem of the minimum energy path. To compute the minimum energy path efficiently and robustly, we developed a pre-training strategy that includes the maximum flux path loss in the early training stage, significantly accelerating the computation of minimum energy and action paths. We demonstrate the superior performance of this method through various analytical and chemical examples, as well as the two- and four-dimensional Ginzburg-Landau functional energy.

physics.chem-ph↗

arXiv2022

Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network

Adaptive training methods for Physics-informed neural network (PINN) require dedicated constructions of the distribution of weights assigned at each training sample. To efficiently seek such an optimal weight distribution is not a simple task and most existing methods choose the adaptive weights based on approximating the full distribution or the maximum of residuals. In this paper, we show that the bottleneck in the adaptive choice of samples for training efficiency is the behavior of the tail distribution of the numerical residual. Thus, we propose the Residual-Quantile Adjustment (RQA) method for a better weight choice for each training sample. After initially setting the weights proportional to the $p$-th power of the residual, our RQA method reassign all weights above $q$-quantile ($90\%$ for example) to the median value, so that the weight follows a quantile-adjusted distribution derived from the residuals. This iterative reweighting technique, on the other hand, is also very easy to implement. Experiment results show that the proposed method can outperform several adaptive methods on various partial differential equation (PDE) problems.

cs.LG↗

arXiv2021

Value-Gradient based Formulation of Optimal Control Problem and Machine Learning Algorithm

Optimal control problem is typically solved by first finding the value function through Hamilton-Jacobi equation (HJE) and then taking the minimizer of the Hamiltonian to obtain the control. In this work, instead of focusing on the value function, we propose a new formulation for the gradient of the value function (value-gradient) as a decoupled system of partial differential equations in the context of continuous-time deterministic discounted optimal control problem. We develop an efficient iterative scheme for this system of equations in parallel by utilizing the properties that they share the same characteristic curves as the HJE for the value function. For the theoretical part, we prove that this iterative scheme converges linearly in $L_α^2$ sense for some suitable exponent $α$ in a weight function. For the numerical method, we combine characteristic line method with machine learning techniques. Specifically, we generate multiple characteristic curves at each policy iteration from an ensemble of initial states, and compute both the value function and its gradient simultaneously on each curve as the labelled data. Then supervised machine learning is applied to minimize the weighted squared loss for both the value function and its gradients. Experimental results demonstrate that this new method not only significantly increases the accuracy but also improves the efficiency and robustness of the numerical estimates, particularly with less amount of characteristics data or fewer training steps.

math.OC↗