arXiv subjects

Yu Jin

Publications and source records attributed to Yu Jin.

At least 19 recordsLinked to original sources

arXiv2026

First-principles cumulant approach to the vibronic structure of spin defects

Color centers in wide-band-gap semiconductors are leading platforms for solid-state quantum technologies, yet a quantitative description of their vibronic structure has remained elusive due to the complexity of multi-phonon processes in localized defect states. Here we present a first-principles Green's function framework based on the retarded cumulant ansatz (RCA) to describe electron-phonon interactions in spin defects; our approach goes beyond the adiabatic and lowest-order perturbation theory approximations underlying widely used approaches. Applied to the negatively charged nitrogen-vacancy (NV$^-$) center in diamond, our method reveals that multi-phonon satellites persist over a 400 meV energy window even at zero temperature, driven by quantum zero-point fluctuations. We demonstrate that accurate spectral functions require mode-, momentum-, spin-, and orbital-resolved electron-phonon matrix elements sampled across the full Brillouin zone, to account for hybridized and propagating phonon channels. We find that the vibronic structure of the NV$^-$ center exhibits strong spin and orbital anisotropy, with different orbitals coupling to qualitatively distinct parts of the phonon spectrum, and spin-selective coupling affecting both sideband positions and intensities.

cond-mat.mtrl-sci↗

arXiv2026

Analytical Forces from the Bethe-Salpeter Equation for Large-Scale Excited-State Relaxation

We present an efficient plane-wave implementation of analytical nuclear forces for electronic excited states described by the Bethe-Salpeter equation (BSE). The formulation combines density-matrix perturbation theory with a Lagrangian approach, and avoids both explicit empty-state summations and the response calculations for each atomic displacement, required by conventional approaches based on density functional perturbation theory. Together with GPU acceleration, these advances make BSE forces calculations tractable for solid-state systems containing hundreds of atoms. We demonstrate the method on two point defects with distinct dielectric environments: the nitrogen-vacancy center in diamond, where BSE and time-dependent density functional theory (TDDFT) yield consistent excited-state relaxations, and the carbon-dimer defect in two-dimensional hexagonal boron nitride, where the screened electron-hole interaction included in the BSE stabilizes the localized defect excitation and corrects the relaxation pattern predicted by semilocal TDDFT. These results establish a scalable framework for BSE-level studies of excited-state relaxation and vibronic coupling in heterogeneous condensed systems.

cond-mat.mtrl-sci↗

arXiv2026

The WEST code for large-scale excited-state materials simulations

We present WEST, an open-source plane-wave pseudopotential code for large-scale excited-state materials simulations, and describe its theoretical foundations, software architecture, and capabilities. WEST implements full-frequency GW, quantum defect embedding theory, the Bethe-Salpeter equation, and time-dependent density functional theory within a common algorithmic framework that avoids the explicit computation of virtual electronic states. By combining density functional and density matrix perturbation theory, low-rank representations of the dielectric screening and exact exchange, and localization techniques, WEST achieves favorable computational scaling with system size. The code supports the calculation of quasi-particle and neutral excitation energies, optical and photoluminescence spectra, excited-state forces, and non-adiabatic couplings, with interoperable workflows connecting to quantum chemistry, vibronic coupling, and quantum computing packages. A hierarchical parallelization strategy and GPU acceleration deliver near-ideal strong scaling to thousands of GPUs, enabling accurate excited-state simulations of systems with more than a thousand atoms. Representative applications, spanning the full optical cycle of solid-state spin defects, self-trapped excitons in metal-halide perovskites, and the optical response of liquid water and ice, demonstrate the accuracy and versatility of the code across diverse material classes. The capabilities implemented in WEST establish the code as a scalable platform for predictive excited-state simulations, high-throughput materials discovery, and the generation of high-fidelity datasets for machine learning in computational materials science.

cond-mat.mtrl-sci↗

arXiv2026

High-performance parallel implementation of high-order coupled-cluster theories

High-order coupled-cluster theories with iterative triples (CCSDT), perturbative quadruples [CCSDT(Q)], and iterative quadruples (CCSDTQ) provide benchmark-quality correlation energies, but their steep computational scalings, $O(N^8), O(N^9)$, and $O(N^{10})$, together with the large memory requirements of high-order amplitude tensors, have historically limited their application to small molecules. In this work, we develop efficient open-source implementations of spin-restricted CCSDT (RCCSDT), RCCSDT(Q), RCCSDTQ, and spin-unrestricted CCSDT (UCCSDT) within the PySCF package. The shared-memory implementation combines compact triangular storage of the highest-order amplitude tensors with the multithreaded tensor contraction backend pytblis, enabling efficient use of modern many-core CPU architectures. This design delivers near-ideal thread scaling up to 90 cores and achieves wall times shorter than or comparable to existing single-node implementations for representative benchmark molecules. We further extend RCCSDT, RCCSDT(Q), and RCCSDTQ to distributed-memory architectures using MPI-based algorithms. By distributing compact high-order amplitudes across MPI ranks and overlapping communication with computation through nonblocking data transfers, the distributed implementation achieves near-ideal strong scaling on up to 32 nodes, corresponding to approximately 3,000 CPU cores. These developments substantially extend the practical reach of canonical high-order CC theory, enabling CCSDT(Q) calculations with approximately 100 correlated electrons in 450 orbitals and CCSDTQ calculations with approximately 50 correlated electrons in 115 orbitals. Applications to $π$-stacked noncovalent dimers, the CO dissociation energy of Cr(CO)$_6$, and the Cope rearrangement of semibullvalene demonstrate that canonical high-order CC benchmarks are now feasible for chemically realistic molecular systems.

physics.chem-ph↗

arXiv2026

A Double Bind: Gendered Funding, Research Topics, and Academic Performance in the Social Sciences

While female representation in social sciences is increasing, systemic gender disparities may persist in research funding and academic performance. Some argue that female scholars now receive equal opportunities, yet evidence suggests that gender imbalances remain, particularly in specific research areas. This study examines 12,945 National Science Foundation (NSF)-funded principal investigators in social sciences from 2000 to 2019 to assess gender disparities in grant allocation, research topics, and post-award academic performance. Findings reveal a dual imbalance. First, despite similar overall funding success rates, female scholars remain underrepresented in high-impact and traditionally male-dominated research topics. Male recipients are more represented in most funded topics, especially technology- and methodology-related ones, whereas female recipients are more concentrated in a smaller set of topics related to children, family, cognition, and health. Second, post-award performance patterns suggest that females outperform males in male-dominated fields, whereas males excel in female-dominated ones, undermining any presumed advantage of female scholars in their own research areas. These patterns may be associated with gendered constraints in academic career trajectories. Furthermore, early-career experiences shape these outcomes asymmetrically. In male-dominated topics, postdoctoral experience is associated with lower publication and citation performance for women but higher publication and citation performance for men. In female-dominated topics, postdoctoral experience is positively associated with women's publications and citations and with men's publication output. These findings suggest that policy discussions should consider not just overall funding equality, but also gendered disparities across research topics and career trajectories.

cs.DL↗

arXiv2026

First-principles calculations of internal conversion processes in spin defects

Optically active spin defects are foundational for quantum technologies, yet common approximations underestimate their internal conversion (IC) rates by orders of magnitude. We propose a broad, predictive framework to compute IC rates that incorporates multi-configurational effects via many-body wavefunctions in TDDFT, and includes all-phonon-mode contributions via analytical non-adiabatic couplings. Our approach resolves discrepancies with experiment, achieving quantitative agreement for the NV$^-$ center in diamond, and identifying a previously overlooked non-radiative channel in the divacancy triplet lifetime in SiC.

cond-mat.mtrl-sci↗

arXiv2026

The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks.

physics.chem-ph↗

arXiv2026

Elucidating the Inter-system Crossing of the Nitrogen-Vacancy Center up to Megabar Pressures

The integration of Nitrogen-Vacancy color centers into diamond anvil cells has opened the door to quantum sensing at megabar pressures. Despite a multitude of experimental demonstrations and applications ranging from quantum materials to geophysics, a detailed microscopic understanding of how stress affects the NV center remains lacking. In this work, using a combination of first principles calculations as well as high-pressure NV experiments, we develop a complete description of the NV's optical properties under general stress conditions. In particular, our ab initio calculations reveal the complex behavior of the NV's inter-system crossing rates under stresses that both preserve and break the defect's symmetry. Crucially, our proposed framework immediately resolves a number of open questions in the field, including: (i) the microscopic origin of the observed contrast-enhancement in (111)-oriented anvils, and (ii) the surprising observation of NV contrast-inversion in certain high-pressure regimes. Our work lays the foundation for optimizing the performance of NV high-pressure sensors by controlling the local stress environment, and more generally, suggests that symmetry-breaking stresses can be utilized as a novel tuning knob for generic solid-state spin defects.

quant-ph↗

arXiv2026

Towards dislocation-driven quantum interconnects

A central problem in the deployment of quantum technologies is the realization of robust architectures for quantum interconnects. We propose to engineer interconnects in semiconductors and insulators by patterning spin qubits at dislocations, thus forming quasi one-dimensional lines of entangled point defects. To gain insight into the feasibility and control of dislocation-driven interconnects, we investigate the optical cycle and coherence properties of nitrogen-vacancy (NV) centers in diamond, in proximity of dislocations, using a combination of advanced first-principles calculations. We show that one can engineer spin defects with properties similar to those of their bulk counterparts, including charge stability and a favorable optical cycle, and that NV centers close to dislocations have much improved coherence properties. Finally, we predict optically detected magnetic resonance spectra that may facilitate the experimental identification of specific defect configurations. Our results provide a theoretical foundation for the engineering of one-dimensional arrays of spin defects in the solid state.

cond-mat.mtrl-sci↗

arXiv2026

From Edge to Edge: A Flow-Inspired Scheduling Planner for Multi-Robot Systems

Trajectory planning is crucial in multi-robot systems, particularly in environments with numerous obstacles. While extensive research has been conducted in this field, the challenge of coordinating multiple robots to flow collectively from one side of the map to the other-such as in crossing missions through obstacle-rich spaces-has received limited attention. This paper focuses on this directional traversal scenario by introducing a real-time scheduling scheme that enables multi-robot systems to move from edge to edge, emulating the smooth and efficient flow of water. Inspired by network flow optimization, our scheme decomposes the environment into a flow-based network structure, enabling the efficient allocation of robots to paths based on real-time congestion levels. The proposed scheduling planner operates on top of existing collision avoidance algorithms, aiming to minimize overall traversal time by balancing detours and waiting times. Simulation results demonstrate the effectiveness of the proposed scheme in achieving fast and coordinated traversal. Furthermore, real-world flight tests with ten drones validate its practical feasibility. This work contributes a flow-inspired, real-time scheduling planner tailored for directional multi-robot traversal in complex, obstacle-rich environments. Code: https://github.com/chengji253/FlowPlanner

cs.RO↗

arXiv2025

Exact Recovery of Non-Random Missing Multidimensional Time Series via Temporal Isometric Delay-Embedding Transform

Non-random missing data is a ubiquitous yet undertreated flaw in multidimensional time series, fundamentally threatening the reliability of data-driven analysis and decision-making. Pure low-rank tensor completion, as a classical data recovery method, falls short in handling non-random missingness, both methodologically and theoretically. Hankel-structured tensor completion models provide a feasible approach for recovering multidimensional time series with non-random missing patterns. However, most Hankel-based multidimensional data recovery methods both suffer from unclear sources of Hankel tensor low-rankness and lack an exact recovery theory for non-random missing data. To address these issues, we propose the temporal isometric delay-embedding transform, which constructs a Hankel tensor whose low-rankness is naturally induced by the smoothness and periodicity of the underlying time series. Leveraging this property, we develop the \textit{Low-Rank Tensor Completion with Temporal Isometric Delay-embedding Transform} (LRTC-TIDT) model, which characterizes the low-rank structure under the \textit{Tensor Singular Value Decomposition} (t-SVD) framework. Once the prescribed non-random sampling conditions and mild incoherence assumptions are satisfied, the proposed LRTC-TIDT model achieves exact recovery, as confirmed by simulation experiments under various non-random missing patterns. Furthermore, LRTC-TIDT consistently outperforms existing tensor-based methods across multiple real-world tasks, including network flow reconstruction, urban traffic estimation, and temperature field prediction. Our implementation is publicly available at https://github.com/HaoShu2000/LRTC-TIDT.

cs.LG↗

arXiv2025

Defects at Play: Shaping the Photophysics and Photochemistry of Ice

The mechanisms by which light interacts with ice and the impact of photo-induced reactions are central to our understanding of environmental, atmospheric and astrophysical processes. However, a microscopic description of the photoproducts originating from UV absorption and emission processes has remained elusive. Here we explore the photochemistry of ice using time-dependent hybrid density functional theory on various models of pristine and defective ice Ih. Our investigation of the excited state potential energy surface of the crystal shows that UV absorption can lead to the formation of hydronium ions, hydroxyl radicals and excess electrons. One of the dominant mechanisms of decay from the excited to the ground-state involves the recombination of the electron with the hydroxyl radical yielding hydronium-hydroxide ion-pairs. We find that the details of this charge recombination process sensitively depend on the presence of defects in the lattice, such as vacancies and pre-existing photoproducts. We also observe the formation of Bjerrum defects following UV absorption; we suggest that, together with hydroxide anions, they are likely responsible for prominent features experimentally detected in long UV exposure absorption spectra, remarkably red-shifted relative to short exposure spectra. Our results highlight the key role of defects in determining the onset of absorption and emission processes in ice.

physics.chem-ph↗

arXiv2025

Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models

We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves both discovering logical facts and inducing logical rules from a small number of unstructured textual or visual inputs, which still remain challenging when solely relying on ILP, due to the requirement of specified background knowledge and high computational cost, or MLLMs, due to the appearance of perceptual hallucinations. Based on the key observation that MLLMs could propose structure-correct rules even under hallucinations, our approach automatically builds ILP tasks with pruned search spaces based on the rule structure proposals from MLLMs, and utilizes ILP system to output rules built upon rectified logical facts and formal inductive reasoning. Its effectiveness is verified through challenging logical induction benchmarks, as well as a potential application of our approach, namely text-to-image customized generation with rule induction. Our code and data are released at https://github.com/future-item/ILP-CoT.

cs.LG↗

arXiv2025

Advances in Quantum Defect Embedding Theory

Quantum defect embedding theory (QDET) is a many-body embedding method designed to describe condensed systems with correlated electrons localized within a given region of space, for example spin defects in semiconductors and insulators. Although the QDET approach has been successful in predicting the electronic properties of several point defects, several limitations of the method remain. In this work, we propose multiple advances to the QDET formalism. We derive a double-counting correction that consistently treats the frequency dependence of the screened Coulomb interaction, and we illustrate the effect of including unoccupied orbitals in the active space. In addition, we propose a method to describe hybridization effects between the active space and the environment, and we compare the results of several impurity solvers, providing further insights into improving the reliability and applicability of the method. We present results for defects in diamond and for molecular qubits, including a detailed comparison with experiments.

cond-mat.mtrl-sci↗

arXiv2025

First-Principles Framework for the Prediction of Intersystem Crossing Rates in Spin Defects: The Role of Electron Correlation

Optically active spin defects in solids are promising platforms for quantum technologies. Here, we present a first-principles framework to investigate intersystem crossing processes, which represent crucial steps in the optical spin-polarization cycle used to address spin defects. Considering the nitrogen-vacancy center in diamond as a case study, we demonstrate that our framework effectively captures electron correlation effects in the calculation of many-body electronic states and their spin-orbit coupling and electron-phonon interactions, while systematically addressing finite-size effects. We validate our predictions by carrying out measurements of fluorescence lifetimes, finding excellent agreement between theory and experiments. The framework presented here provides a versatile and robust tool for exploring the optical cycle of varied spin defects entirely from first principles.

cond-mat.mtrl-sci↗

arXiv2025

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings

Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method are introduced to enhance the controllability of logical symbols over generation. Furthermore, we propose two logical abduction methods to make our approach require few labeled training data and support the induction of latent logical generative rules from data. We experimentally show that our approach can be utilized to integrate various neural generative models with logical reasoning systems, by both learning from scratch or utilizing pre-trained models directly. The code is released at https://github.com/future-item/AbdGen.

cs.AI↗

arXiv2025

Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection

Event-based Vision Sensors (EVS) have demonstrated significant advantages over traditional RGB frame-based cameras in low-light conditions, high-speed motion capture, and low latency. Consequently, object detection based on EVS has attracted increasing attention from researchers. Current event stream object detection algorithms are typically built upon Convolutional Neural Networks (CNNs) or Transformers, which either capture limited local features using convolutional filters or incur high computational costs due to the utilization of self-attention. Recently proposed vision heat conduction backbone networks have shown a good balance between efficiency and accuracy; however, these models are not specifically designed for event stream data. They exhibit weak capability in modeling object contour information and fail to exploit the benefits of multi-scale features. To address these issues, this paper proposes a novel dynamic graph induced contour-aware heat conduction network for event stream based object detection, termed CvHeat-DET. The proposed model effectively leverages the clear contour information inherent in event streams to predict the thermal diffusivity coefficients within the heat conduction model, and integrates hierarchical structural graph features to enhance feature learning across multiple scales. Extensive experiments on three benchmark datasets for event stream-based object detection fully validated the effectiveness of the proposed model. The source code of this paper will be released on https://github.com/Event-AHU/OpenEvDET.

cs.CV↗

arXiv2025

Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning

Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual generation process is guided by the induced logic rules. A major challenge for this task is to reduce the time cost of logic abduction during learning, an essential step when the logic symbol set is large and the logic rule to induce is complicated. To address this challenge, we propose a pre-training method for obtaining meta-rule selection policy for the recently proposed visual generative learning approach AbdGen [Peng et al., 2023], aiming at significantly reducing the candidate meta-rule set and pruning the search space. The selection model is built based on the embedding representation of both symbol grounding of cases and meta-rules, which can be effectively integrated with both neural model and logic reasoning system. The pre-training process is done on pure symbol data, not involving symbol grounding learning of raw visual inputs, making the entire learning process low-cost. An additional interesting observation is that the selection policy can rectify symbol grounding errors unseen during pre-training, which is resulted from the memorization ability of attention mechanism and the relative stability of symbolic patterns. Experimental results show that our method is able to effectively address the meta-rule selection problem for visual abduction, boosting the efficiency of visual generative abductive learning. Code is available at https://github.com/future-item/metarule-select.

cs.LG↗