arXiv subjects

Tianrui Chen

Publications and source records attributed to Tianrui Chen.

7 recordsLinked to original sources

arXiv2026

Quantum Vibronic Dynamics Shape Catalytically Relevant Au-Ligand Interfaces in Atomically Precise Gold Nanoclusters

Atomically precise gold nanoclusters are versatile for photocatalysis and energy conversion because their electronic structure stems from strong metal-ligand interactions. However, these interactions are mostly discussed statically, leaving dynamic reorganization of Au-ligand interfaces under photoexcitation unclear. We investigate rod-shaped [Au25(PPh3)10(SC2H5)5Cl2]2+ using ultrafast transient-grating spectroscopy, two-dimensional electronic spectroscopy, ab initio calculations, and hierarchical equations-of-motion simulations. The multidimensional spectra resolve multiple electronic relaxation pathways and a hierarchy of coherent structural motions, from localized Au-ligand distortions to collective framework vibrations. Wavelet analysis reveals that high-frequency Au-ligand vibrations emerge immediately after excitation, whereas low-frequency collective modes appear later through interstate vibronic coupling, indicating sequential redistribution of structural coherence. Simulations reproduce the nonlinear response and identify the microscopic vibronic couplings responsible. The results show that photoexcitation drives continuous ultrafast reorganization of the Au-ligand bonding network, transiently reshaping interfacial electronic structure before thermalization. This work establishes dynamic Au-ligand interfaces as the microscopic link between excited-state energy flow and photochemical function in atomically precise nanoclusters.

physics.chem-ph↗

arXiv2026

Environmental Control Extends Beyond Quantum Dephasing in Exciton Energy Transfer

Excitation-energy transfer underpins the conversion of light into usable energy in photosynthetic organisms and serves as a paradigm for evolutionary optimized transport in open quantum systems. Although this process is often described as incoherent thermally assisted hopping, such descriptions become inadequate when electronic coupling, vibronic interactions and environmental fluctuations occur on comparable energy scales. Determining how the environment controls transport therefore remains a fundamental challenge. Here, we use temperature-dependent 2DES to investigate energy transfer in the photosynthetic antenna protein allophycocyanin over the range 10 - 296 K. The dominant $β\rightarrow α$ transfer step exhibits a pronounced non-monotonic temperature dependence: the transfer time decreases from 400 fs at 10 K to 200 fs near 30- 40 K before increasing again to 400 fs at 296 K. In contrast, the homogeneous optical dephasing time decreases monotonically across the same temperature range. To interpret these observations, we model APC as a vibronically coupled excitonic dimer interacting with a structured environment and solve the dynamics using hierarchical equations of motion. Conventional fixed-bath models, including Drude-Lorentz and explicit intermolecular-mode spectral densities, fail to reproduce the observed turnover. Quantitative agreement is obtained only when the low-frequency sector of the environmental spectral density is allowed to anharmonically evolve strongly with temperature, while the high-frequency bath remains essentially unchanged. More broadly, these findings demonstrate that transport efficiency is controlled not simply by the magnitude of environmental fluctuations, but by the distribution of environmental spectral weight across frequency space, providing new experimental constraints on theories of molecular transport in complex quantum environments.

physics.chem-ph↗

arXiv2026

Conical Intersections Enable Ultrafast Molecular Spin Control in a Chromium Complex

Molecular spintronics seeks to control spin states in single molecules for ultrafast switching and efficient information processing. Transition metal complexes are promising candidates for such applications due to their modular ligand fields, diverse spin configurations, and potential for spin-vibronic coupling that facilitates rapid spin dynamics. Chromium(III) complexes, in particular, offer long-lived emissive doublet states and chemical robustness, making them attractive for room-temperature spin control. Here we investigate the spin-state dynamics of tris(2,4-pentanedionato)chromium(III), [Cr(acac)3], a photochemically stable d3 complex with minimal vibrational congestion. Using ultrafast transient grating and two dimensional electronic spectroscopy with ~10 fs resolution, we directly probe vibrational and electronic dynamics associated with the 4T2 -> 2E intersystem crossing (ISC). These measurements reveal coherent vibrational modes implicated in mediating nonadiabatic spin transitions. Complementary theoretical modelling shows that vibronic coupling and spin orbit interactions promote the formation of multiple conical intersections, providing ultrafast channels for spin-flip dynamics. Metal-ligand bending and stretching modes serve as tuning and coupling coordinates, enabling ISC despite weak spin-orbit coupling in 3d transition metal. Our study provides mechanistic insight into spin-vibronic dynamics in Cr(III) complexes and establishes a design framework for achieving ultrafast molecular spin switching, advancing the development of optically addressable spin centres for future spintronic and quantum technologies.

physics.chem-ph↗

arXiv2024

Safe Linear Bandits over Unknown Polytopes

The safe linear bandit problem (SLB) is an online approach to linear programming with unknown objective and unknown roundwise constraints, under stochastic bandit feedback of rewards and safety risks of actions. We study the tradeoffs between efficacy and smooth safety costs of SLBs over polytopes, and the role of aggressive doubly-optimistic play in avoiding the strong assumptions made by extant pessimistic-optimistic approaches. We first elucidate an inherent hardness in SLBs due the lack of knowledge of constraints: there exist `easy' instances, for which suboptimal extreme points have large `gaps', but on which SLB methods must still incur $Ω(\sqrt{T})$ regret or safety violations, due to an inability to resolve unknown optima to arbitrary precision. We then analyse a natural doubly-optimistic strategy for the safe linear bandit problem, DOSS, which uses optimistic estimates of both reward and safety risks to select actions, and show that despite the lack of knowledge of constraints or feasible points, DOSS simultaneously obtains tight instance-dependent $O(\log^2 T)$ bounds on efficacy regret, and $\tilde O(\sqrt{T})$ bounds on safety violations. Further, when safety is demanded to a finite precision, violations improve to $O(\log^2 T).$ These results rely on a novel dual analysis of linear bandits: we argue that \algoname proceeds by activating noisy versions of at least $d$ constraints in each round, which allows us to separately analyse rounds where a `poor' set of constraints is activated, and rounds where `good' sets of constraints are activated. The costs in the former are controlled to $O(\log^2 T)$ by developing new dual notions of gaps, based on global sensitivity analyses of linear programs, that quantify the suboptimality of each such set of constraints. The latter costs are controlled to $O(1)$ by explicitly analysing the solutions of optimistic play.

cs.LG↗

arXiv2024

3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis

In this paper, we propose a 3D geometry-aware deformable Gaussian Splatting method for dynamic view synthesis. Existing neural radiance fields (NeRF) based solutions learn the deformation in an implicit manner, which cannot incorporate 3D scene geometry. Therefore, the learned deformation is not necessarily geometrically coherent, which results in unsatisfactory dynamic view synthesis and 3D dynamic reconstruction. Recently, 3D Gaussian Splatting provides a new representation of the 3D scene, building upon which the 3D geometry could be exploited in learning the complex 3D deformation. Specifically, the scenes are represented as a collection of 3D Gaussian, where each 3D Gaussian is optimized to move and rotate over time to model the deformation. To enforce the 3D scene geometry constraint during deformation, we explicitly extract 3D geometry features and integrate them in learning the 3D deformation. In this way, our solution achieves 3D geometry-aware deformation modeling, which enables improved dynamic view synthesis and 3D dynamic reconstruction. Extensive experimental results on both synthetic and real datasets prove the superiority of our solution, which achieves new state-of-the-art performance. The project is available at https://npucvr.github.io/GaGS/

cs.CV↗

arXiv2024

Model-free Optimization and Experimental Validation of RIS-assisted Wireless Communications under Rich Multipath Fading

Reconfigurable intelligent surface (RIS) devices have emerged as an effective way to control the propagation channels for enhancing the end-users' performance. However, RIS optimization involves configuring the radio frequency response of a large number of radiating elements, which is challenging in real-world applications due to high computational complexity. In this paper, a model-free cross-entropy (CE) algorithm is proposed to optimize the binary RIS configuration for improving the signal-to-noise ratio (SNR) at the receiver. One key advantage of the proposed method is that it only requires system performance indicators, e.g., the received SNR, without the need for channel models or channel state information. Both simulations and experiments are conducted to evaluate the performance of the proposed CE algorithm. This study provides an experimental demonstration of the channel hardening effect in a multi-antenna RIS-assisted wireless system under rich multipath fading.

cs.IT↗

arXiv2022

Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk

We investigate a natural but surprisingly unstudied approach to the multi-armed bandit problem under safety risk constraints. Each arm is associated with an unknown law on safety risks and rewards, and the learner's goal is to maximise reward whilst not playing unsafe arms, as determined by a given threshold on the mean risk. We formulate a pseudo-regret for this setting that enforces this safety constraint in a per-round way by softly penalising any violation, regardless of the gain in reward due to the same. This has practical relevance to scenarios such as clinical trials, where one must maintain safety for each round rather than in an aggregated sense. We describe doubly optimistic strategies for this scenario, which maintain optimistic indices for both safety risk and reward. We show that schema based on both frequentist and Bayesian indices satisfy tight gap-dependent logarithmic regret bounds, and further that these play unsafe arms only logarithmically many times in total. This theoretical analysis is complemented by simulation studies demonstrating the effectiveness of the proposed schema, and probing the domains in which their use is appropriate.

cs.LG↗