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Qiangqiang Gu

Publications and source records attributed to Qiangqiang Gu.

At least 19 recordsLinked to original sources

Extracting a nitrile-centered, ether-assisted motif hierarchy for lithium-battery electrolyte design from billion-scale molecular space

Designing electrolyte molecules for lithium batteries requires balancing electronic stability with appropriate Li+ solvation, yet the structural basis remains unclear across chemically diverse molecules. High-throughput screening expands the searchable space, but ranked candidates alone do not reveal recurring motifs or their applicability limits. We searched nearly one billion GDB13 structures using electronic--solvation descriptors without explicit functional-group preferences or scaffold constraints. Across descriptor weights, high-ranking populations separated into a nitrile-dominant regime and a coexistence regime containing substantial fractions of both nitrile- and ether-containing molecules. These regimes together define a nitrile-centered, ether-assisted motif hierarchy: nitrile remains favored across broad weight ranges, whereas ether becomes prominent under stronger electrostatic and polarity constraints. Encoding this hierarchy in a generative model expands the candidate space beyond GDB13 and yields high-scoring fluorinated structures without an explicit fluorination reward. Explicit-solvent molecular dynamics simulations show weak, exchangeable coordination of representative candidates without displacing ethylene carbonate from the dominant first solvation shell around Li+; effects on ion association and transport depend on molecular structure and concentration. These results establish a quantitative, interpretable and physically bounded motif hierarchy that systematizes established nitrile and ether chemistry for lithium-battery electrolyte design.

physics.chem-ph

Direct observation of quadruple spin-texture locking in a 2D d-wave altermagnet

Altermagnets combine vanishing net magnetization with nonrelativistic, momentum-dependent spin splitting, offering a new paradigm for spintronics. Spin-crystal symmetry coupling, namely spin-lattice locking, is the defining mechanism of altermagnetism, enforcing opposite spin sublattices in real space and spin-momentum-locked electronic structure in reciprocal space. Direct atomic-scale visualization of spin-lattice locking therefore constitutes a decisive benchmark of the altermagnetic state, yet such evidence has remained elusive despite extensive efforts. Here we show that the electronic states in RbV2Se2O exhibit a d-wave-like spin texture at the sublattice level, providing the first atomic-scale evidence of spin-lattice locking with a predominantly c-axis spin orientation. By employing an in-situ, field-switchable spin-polarized Cr tip, we realize spin-contrast mapping of quasiparticle interference at identical energies, overcoming a long-standing experimental barrier in altermagnets. The resulting interference patterns exhibit pronounced spin-dependent modulations, establishing spin scattering locking and spin momentum locking as the real and reciprocal space manifestations. Unexpectedly, we uncover that the spin-selective scattering response is organized by a long-period stripe modulation, giving rise to a previously unidentified form of spin-texture locking, spin-stripe locking. We attribute this behavior to the emergence of a spin-density-wave moir\'e pattern. Together, these results establish a unified picture of quadruple spin-texture locking phenomena in a d-wave altermagnet, and position altermagnets as a versatile platform for exploring many-body interactions among intertwined degrees of freedom, including spin, lattice, momentum, moir\'e potential and valley.

cond-mat.mtrl-sci

Automating Computational Chemistry Workflows via OpenClaw and Domain-Specific Skills

This work presents a decoupled framework for multi-step computational chemistry automation built on OpenClaw. OpenClaw serves as the general-purpose agent for task coordination and supervision. Planning skills externalize task descriptions into executable task specifications, domain skills provide computational chemistry procedures, and the DPDispatcher skill grounds computation in heterogeneous HPC environments. In a methane-oxidation reactive MD case study, the framework coordinated cross-tool execution, supported bounded recovery from runtime failures, and extracted reaction networks.

physics.chem-ph

A Lightweight Multi-Cancer Tumor Localization Framework for Deployable Digital Pathology

Accurate localization of tumor regions from hematoxylin and eosin-stained whole-slide images is fundamental for translational research including spatial analysis, molecular profiling, and tissue architecture investigation. However, deep learning-based tumor detection trained within specific cancers may exhibit reduced robustness when applied across different tumor types. We investigated whether balanced training across cancers at modest scale can achieve high performance and generalize to unseen tumor types. A multi-cancer tumor localization model (MuCTaL) was trained on 79,984 non-overlapping tiles from four cancers (melanoma, hepatocellular carcinoma, colorectal cancer, and non-small cell lung cancer) using transfer learning with DenseNet169. The model achieved a tile-level ROC-AUC of 0.97 in validation data from the four training cancers, and 0.71 on an independent pancreatic ductal adenocarcinoma cohort. A scalable inference workflow was built to generate spatial tumor probability heatmaps compatible with existing digital pathology tools. Code and models are publicly available at https://github.com/AivaraX-AI/MuCTaL.

cs.CV

Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale

AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assisted steps toward \emph{agentic science at scale}. This shift is increasingly feasible, as scientific tools and models can be invoked through stable interfaces and verified with recorded execution traces, and increasingly necessary, as AI accelerates scientific output and stresses the peer-review and publication pipeline, raising the bar for traceability and credible evaluation. However, scaling agentic science remains difficult: workflows are hard to observe and reproduce; many tools and laboratory systems are not agent-ready; execution is hard to trace and govern; and prototype AI Scientist systems are often bespoke, limiting reuse and systematic improvement from real workflow signals. We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated in Bohrium+SciMaster. Bohrium acts as a managed, traceable hub for AI4S assets -- akin to a HuggingFace of AI for Science -- that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities. SciMaster orchestrates these capabilities into long-horizon scientific workflows, on which scientific agents can be composed and executed. Between infrastructure and orchestration, a \emph{scientific intelligence substrate} organizes reusable models, knowledge, and components into executable building blocks for workflow reasoning and action, enabling composition, auditability, and improvement through use. We demonstrate this stack with eleven representative master agents in real workflows, achieving orders-of-magnitude reductions in end-to-end scientific cycle time and generating execution-grounded signals from real workloads at multi-million scale.

cs.AI

Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials

Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While density functional theory (DFT) combined with the non-equilibrium Green$'$s function (NEGF) formalism provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonians derived learned directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. To validate the method, we apply it to three prototypical 2D materials: graphene, hexagonal boron nitride (h-BN), and MoS$_2$. The resulting band structures and transmission spectra show excellent agreement with conventional DFT-NEGF results, while achieving orders-of-magnitude improvement in efficiency. These results highlight the capability of DeePTB-NEGF to enable accurate and efficient quantum transport simulations, thereby opening avenues for large-scale exploration and device design in 2D materials.

cond-mat.mtrl-sci

Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening

Discovering nonlinear optical (NLO) materials with strong shift current response, particularly in the infrared (IR) regime, is essential for next-generation optoelectronics yet remains highly challenging in both experiments and theory, which still largely relies on case by case studies. Here, we employ a high-throughput screening strategy, applying a multi-step filter to the Materials Project database (>154,000 materials), which yielded 2,519 candidate materials for detailed first-principle evaluation. From these calculations, we identify 32 NLO materials with strong shift current response ($\sigma$ > 100 $\mu A/V^2$). Our work reveals that layered structures with $C_{3v}$ symmetry and heavy $p$-block elements (e.g. Te, Sb) exhibit apparent superiority in enhancing shift current. More importantly, 9 of these compounds show shift current response peaks in the IR region, with the strongest reaching 616 $\mu A/V^2$, holding significant application potential in fields such as IR photodetection, sensing, and energy harvesting. Beyond identifying promising candidates, this work establishes a comprehensive and high-quality first-principles dataset for NLO response, providing a solid foundation for future AI-driven screening and accelerated discovery of high-performance NLO materials, as demonstrated by a prototype machine-learning application.

cond-mat.mtrl-sci

Odd-Parity Quasiparticle Interference in the Superconductive Surface State of UTe2

Although no known material exhibits intrinsic topological superconductivity, wherein spin-triplet odd-parity electron pairing occurs, UTe2 is now the leading representative of this class. Conventionally, the parity of the superconducting order parameter may be established by using Bogoliubov quasiparticle interference (QPI) imaging. However, odd-parity superconductors should support a topological quasiparticle surface band (QSB) at energies within the maximum superconducting energy gap. QPI would then be dominated by the electronic structure of the QSB and only reveal the characteristics of the bulk order parameter excursively. Here, we visualize quasiparticle interference patterns of UTe2 and find that, at the (0-11) cleave surface, a new band of Bogoliubov quasiparticles appears only in the superconducting state. QPI visualization then allows study of dispersion of states within this QSB, which we demonstrate exists only within the range of Fermi momenta projected onto the (0-11) surface. Finally, we develop a theoretical framework to predict the QPI signatures of such a QSB at the (0-11) surface of UTe2. Its predictions are most consistent with the experimental results if the bulk superconducting gap function exhibits time-reversal conserving, odd-parity, a-axis nodal, B3u symmetry.

cond-mat.supr-con

Quasiparticle interference and spectral function of the UTe$_2$ superconductive surface band

We compute the (0-11) surface spectral function, the surface density of states (DOS), and the quasiparticle interference (QPI) patterns, both in the normal state and superconducting (SC) state of UTe$_2$. We consider all possible non-chiral and chiral order parameters (OPs) that could in principle describe the superconductivity in this compound. We describe the formation of surface states whose maximum intensity energy depends on the nature of the pairing. We study also the QPI patterns resulting from the scattering of these surface states. We show that the main feature distinguishing between various OPs is a QPI peak that is only observed experimentally in the superconducting state. The energy dispersion and the stability of this peak is consistent among the non-chiral OPs only with a $B_{3u}$ pairing. Moreover, $B_{3u}$ is the only non-chiral pairing that shows a peak at zero energy in the DOS, consistent with the experimental observations.

cond-mat.supr-con

Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors

Ab initio based accurate simulation of phonon-assisted optical spectra of semiconductors at finite temperatures remains a formidable challenge, as it requires large supercells for phonon sampling and computationally expensive high-accuracy exchange-correlation (XC) functionals. In this work, we present an efficient approach that combines deep learning tight-binding and potential models to address this challenge with ab initio fidelity. By leveraging molecular dynamics for atomic configuration sampling and deep learning-enabled rapid Hamiltonian evaluation, our approach enables large-scale simulations of temperature-dependent optical properties using advanced XC functionals (HSE, SCAN). Demonstrated on silicon and gallium arsenide across temperature 100-400 K, the method accurately captures phonon-induced bandgap renormalization and indirect/direct absorption processes which are in excellent agreement with experimental findings over five orders of magnitude. This work establishes a pathway for high-throughput investigation of electron-phonon coupled phenomena in complex materials, overcoming traditional computational limitations arising from large supercell used with computationally expensive XC-functionals.

cond-mat.mtrl-sci

Pair Wavefunction Symmetry in UTe2 from Zero-Energy Surface State Visualization

Although nodal spin-triplet topological superconductivity appears probable in UTe2, its superconductive order-parameter $\Delta_k$ remains unestablished. In theory, a distinctive identifier would be the existence of a superconductive topological surface band (TSB), which could facilitate zero-energy Andreev tunneling to an s-wave superconductor, and also distinguish a chiral from non-chiral $\Delta_k$ via enhanced s-wave proximity. Here we employ s-wave superconductive scan-tips and detect intense zero-energy Andreev conductance at the UTe2 (0-11) termination surface. Imaging reveals sub-gap quasiparticle scattering interference signatures with a-axis orientation. The observed zero-energy Andreev peak splitting with enhanced s-wave proximity, signifies that $\Delta_k$ of UTe2 is a non-chiral state: B1u, B2u or B3u. However, if the quasiparticle scattering along the a-axis is internodal, then a non-chiral B3u state is the most consistent for UTe2.

cond-mat.supr-con

ABACUS: An Electronic Structure Analysis Package for the AI Era

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

cond-mat.mtrl-sci

Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors

Quantum transport simulations are essential for understanding and designing nanoelectronic devices, yet the long-standing trade-off between accuracy and computational efficiency has limited their practical applications. We present DeePTB-NEGF, an integrated framework combining deep learning tight-binding Hamiltonian prediction with non-equilibrium Green's Function methodology to enable accurate quantum transport simulations in open boundary conditions with 2-3 orders of magnitude acceleration. We demonstrate DeePTB-NEGF through two challenging applications: comprehensive break junction simulations with over $10^4$ snapshots, showing excellent agreement with experimental conductance histograms; and carbon nanotube field-effect transistors (CNT-FET) at experimental dimensions, reproducing measured transfer characteristics for a 41 nm channel CNT-FET ($\sim 8000$ atoms, $3\times10^4$ orbitals) and predicting zero-bias transmission spectra for a 180 nm CNT ($\sim 3\times 10^4$ atoms, $10^5$ orbitals), showcasing the framework's capability for large-scale device simulations. Our systematic studies across varying geometries confirm the necessity of simulating realistic experimental structures for precise predictions. DeePTB-NEGF bridges the longstanding gap between first-principles accuracy and computational efficiency, providing a scalable tool for high-throughput and large-scale quantum transport simulations that enables previously inaccessible nanoscale device investigations.

cond-mat.mes-hall

Learning local equivariant representations for quantum operators

Predicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in the density functional theory (DFT) framework is crucial for material science. Current methods often focus on individual operators and struggle with efficiency and scalability for large systems. Here we introduce a novel deep learning model, SLEM (strictly localized equivariant message-passing) for predicting multiple quantum operators, that achieves state-of-the-art accuracy while dramatically improving computational efficiency. SLEM's key innovation is its strict locality-based design for equivariant representations of quantum tensors while preserving physical symmetries. This enables complex many-body dependency without expanding the effective receptive field, leading to superior data efficiency and transferability. Using an innovative SO(2) convolution and invariant overlap parameterization, SLEM reduces the computational complexity of high-order tensor products and is therefore capable of handling systems requiring the $f$ and $g$ orbitals in their basis sets. We demonstrate SLEM's capabilities across diverse 2D and 3D materials, achieving high accuracy even with limited training data. SLEM's design facilitates efficient parallelization, potentially extending DFT simulations to systems with device-level sizes, opening new possibilities for large-scale quantum simulations and high-throughput materials discovery.

cond-mat.mtrl-sci

Pitfalls of Exchange-Correlation Functionals in Descriptions of Magnetism: Cautionary Tale of the FeRh Alloy

The magnetic ground state of FeRh is highly sensitive towards the lattice constant. This, in addition to partially filled d-shells of Fe and Rh, posed a significant challenge for Density Functional Theory (DFT) calculations in the past. Here, we have investigated the performance of various exchange-correlation (XC) functionals within the DFT formalism for this challenging binary alloy. We have employed Local Spin Density Approximation (LSDA), various Generalized Gradient Approximations (GGAs), and newly developed Strongly Constrained and Appropriately Normed (SCAN) meta-GGA functional. Our results show the limitations of any single functional in capturing the intricate interplay of structural, electronic, and magnetic properties in FeRh. While SCAN can accurately describe some magnetic features and phonon dispersion, it significantly overestimates the Fe-Fe magnetic interactions, leading to an unreasonable magnetic ordering temperature. Conversely, the Perdew-Burke-Ernzerhof (PBE) GGA exhibits the opposite behavior. These findings highlight the challenges in simulating materials with partially filled $d$-shells using DFT, underscoring the crucial need for developing a versatile XC functional that can effectively account for the multifaceted nature of such systems.

cond-mat.mtrl-sci

Exploration of magnetoelastic deformations in spin-chain compound CuBr$_2$

We investigate a spin-$\frac{1}{2}$ antiferromagnet, CuBr$_2$, which has quasi-one-dimensional structural motifs. The system has previously been observed to exhibit unusual Raman modes possibly due to a locally deformed crystal structure driven by the low-dimensional magnetism. Using hard X-ray scattering and neutron total scattering, here we aim to verify a specific form of tetramerized lattice deformation proposed in the previous study. Apart from diffuse scattering signals which we can reproduce by performing a thorough modeling of the lattice's thermal vibrations, we do not observe evidence for a tetramerized lattice structure within our detection sensitivity. As a result, it is more likely that the unusual Raman modes in CuBr$_2$ arise from classical magnon-phonon hybridization, rather than from quantum spin-singlet-driven lattice deformation.

cond-mat.str-el

Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with $ab$ $initio$ accuracy

Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with $ab$ $initio$ accuracy to address this issue. By training on structural data and corresponding $ab$ $initio$ eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with $10^6$ atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.

cond-mat.mtrl-sci

Observation of 1D Fermi arc states in Weyl semimetal TaAs

Fermi arcs on Weyl semimetals exhibit many exotic quantum phenomena. Usually considered on atomically-flat surfaces with approximate translation symmetry, Fermi arcs are rooted in peculiar topology of bulk Bloch bands of three-dimensional (3D) crystals. The fundamental question of whether a 1D Fermi arc can be probed remains unanswered. Such answer could significantly broaden potential applications of Weyl semimetals. Here, we report a direct observation of robust edge states on atomic-scale ledges in TaAs using low-temperature scanning tunneling microscopy/spectroscopy. Spectroscopic signatures and theoretical calculations reveal that the 1D Fermi arcs arise from the chiral Weyl points of bulk crystal. The crossover from 2D Fermi arcs to eventual complete localization on 1D edges was arrested experimentally on a sequence of surfaces. Our results demonstrate extreme robustness of the bulk-boundary correspondence, which offers topological protection for Fermi arcs, even in cases in which the boundaries are down to atomic-scale. The persistent 1D Fermi arcs can be profitably exploited in miniaturized quantum devices.

cond-mat.mes-hall