arXiv subjects

Xiaofeng Qian

Publications and source records attributed to Xiaofeng Qian.

At least 19 recordsLinked to original sources

arXiv2026

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within predefined workflows, we aim to automate workflow planning given high-level goals and scientist intuition. To this end, we propose Materials Agent unifying Planning, Physics, and Scientists, known as MAPPS. MAPPS consists of a Workflow Planner, a Tool Code Generator, and a Scientific Mediator. The Workflow Planner uses large language models (LLMs) to generate structured and multi-step workflows. The Tool Code Generator synthesizes executable Python code for various tasks, including invoking a force field foundation model that encodes physics. The Scientific Mediator coordinates communications, facilitates scientist feedback, and ensures robustness through error reflection and recovery. By unifying planning, physics, and scientists, MAPPS enables flexible and reliable materials discovery with greater autonomy, achieving a five-fold improvement in stability, uniqueness, and novelty rates compared with prior generative models when evaluated on the MP-20 data. We provide extensive experiments across diverse tasks to show that MAPPS is a promising framework for autonomous materials discovery.

cs.AI↗

arXiv2026

Spin-Weighted Spherical Harmonics Enable Complete and Scalable $\mathrm{E}(3)$-Equivariant Networks

$\mathrm{E}(3)$-equivariant networks are promising for 3D atomistic system modeling, yet their scalability is limited by the $O(L^6)$ complexity of the Clebsch-Gordan Tensor Product (CGTP). The recently proposed Gaunt Tensor Product (GTP) reduces the complexity but is unable to capture the antisymmetric paths, resulting in incomplete expressivity. In this work, we present SpinGTP, an approach to overcome the GTP incompleteness by generalizing from scalar functions to Spin-Weighted Spherical Harmonics (SWSH). By relying on the algebraic properties of SWSH, SpinGTP recovers the missing antisymmetric interactions while maintaining the asymptotic efficiency of GTP. It also allows for a more expressive equivariant basis that naturally accounts for the parity-odd components of tensor products. We evaluate SpinGTP across diverse benchmarks, including Tetris, 3BPA, SPICE-MACE-OFF, and OC20. Our results show that SpinGTP achieves accuracies comparable to full CGTP. Notably, by explicitly capturing antisymmetric paths, SpinGTP exhibits superior performance in tasks involving chiral materials and non-centrosymmetric geometries. This work provides a complete, scalable, and mathematically rigorous path toward high-order equivariance in large-scale 3D atomistic system simulations.

cs.LG↗

arXiv2026

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames

We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set of new efficient and powerful SO(2)-equivariant operations and performing all off-diagonal feature updates and message passing within SO(2) local frames, thereby eliminating the need of SO(3) tensor products. Moreover, a continuous SO(2) tensor product is performed within the SO(2) local frame at each node to fuse node features, mimicking the symmetric contraction operation. Extensive experiments on the large QH9 and MD17 datasets demonstrate that our model achieves superior performance across a wide range of molecular structures and trajectories, highlighting its strong generalization capability. The proposed SO(2) operations on SO(2) local frames offer a promising direction for scalable and symmetry-aware learning of electronic structures. Our code will be released as part of the AIRS library https://github.com/divelab/AIRS.

cs.LG↗

arXiv2026

Ising models on the hydrogen peroxide and other lattices

We perform a Monte Carlo analysis of the Ising model on many three-dimensional lattices. By means of finite-size scaling we obtain the critical points and determine the scaling dimensions. As expected, the critical exponents agree with the three-dimensional Ising universality class for all models. The irrelevant field, as revealed by the correction-to-scaling amplitudes, appears to be relatively large. Combining the Monte Carlo results for the hydrogen peroxide lattice with those for five other three-dimensional lattices, we obtain a set of data covering a wide range of the irrelevant temperature field. This is helpful in the determination of the parameters describing the corrections to scaling. As a consequence, new results are obtained for the universal parameters describing Ising criticality in three dimensions, with reduced error margins in comparison with earlier Monte Carlo analyses. The critical exponents describing the thermodynamic singularities are determined by the temperature renormalization exponent $y_t = 1.58693 (9)$ and the magnetic renormalization exponent $y_h = 2.48178 (5)$. The corrections to scaling are governed by the irrelevant exponent $y_1 = -0.821 (5)$.

cond-mat.stat-mech↗

arXiv2026

Hot biexcitons driven by extreme optical confinement

A powerful means to understanding condensed matter that possesses a multi-constituent, non-isolated, and complex nature, with a preeminent example being two-dimensional (2D) materials, is studying many-body interactions. However, experimentally observing high-order many-body interactions is a daunting task due to its heavy reliance on the abundance of low-order complexes. Here, we report the observation of four-body hot biexcitons in an energetically unfavorable bilayer of tungsten disulfide (WS2) through creating extreme optical confinement. Specifically, we integrate a non-radiative bound state in the continuum (BIC) into a photonic crystal (PhC) defect cavity, forming a quasi-three-dimensional (q-3D) but open confinement for photons at the driving frequency. The extremely confined photons in both reciprocal and physical spaces then excite inherently unproductive two-body hot excitons situated slightly above the indirect bandgap so efficiently that they form overwhelmed higher-order four-body hot biexcitons. Distinctively, these hot biexcitons exhibit substantial valley polarization and coherence at room temperature, which we attribute to the topological nature of BICs and the associated q-3D confinement with an orbital angular momentum. Besides achieving room-temperature biexcitons, the q-3D confinement could be valuable for higher-order interactions, such as triexcitons, and many other many-body phenomena, including Bose-Einstein condensation.

physics.optics↗

arXiv2026

Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory

We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunctions of a molecule evolve over time in response to an external excitation, enabling first-principles predictions of physical properties such as optical absorption, electron dynamics, and high-order response. However, conventional real-time TDDFT relies on time-consuming propagation of all occupied states with fine time steps. In this work, we propose OrbEvo, which is based on an equivariant graph transformer architecture and learns to evolve the full electronic wavefunction coefficients across time steps. First, to account for external field, we design an equivariant conditioning to encode both strength and direction of external electric field and break the symmetry from SO(3) to SO(2). Furthermore, we design two OrbEvo models, OrbEvo-WF and OrbEvo-DM, using wavefunction pooling and density matrix as interaction method, respectively. Motivated by the central role of the density functional in TDDFT, OrbEvo-DM encodes the density matrix aggregated from all occupied electronic states into feature vectors via tensor contraction, providing a more intuitive approach to learn the time evolution operator. We adopt a training strategy specifically tailored to limit the error accumulation of time-dependent wavefunctions over autoregressive rollout. To evaluate our approach, we generate TDDFT datasets consisting of 5,000 different molecules in the QM9 dataset and 1,500 molecular configurations of the malonaldehyde molecule in the MD17 dataset. Results show that our OrbEvo model accurately captures quantum dynamics of excited states under external field, including time-dependent wavefunctions, time-dependent dipole moment, and optical absorption spectra.

cs.LG↗

arXiv2026

Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics

Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to as agents, that accelerate discovery across varying levels of autonomy. These language agents provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics. This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle, from hypothesis discovery, experimental design and execution, to result analysis and refinement. We critically examine current methodologies, emphasizing key innovations, practical achievements, and outstanding limitations. Additionally, we identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents. Our analysis highlights the transformative potential of autonomous agents to accelerate scientific discovery across diverse domains.

cs.AI↗

arXiv2026

Suppression of Metallic Transport in Nitrogen-rich Two-Dimensional Transition Metal Nitrides

The recent experimental realization of two-dimensional (2D) transition metal nitrides (TMNs, e.g., Mo5N6, δ-MoN, and W5N6) opens new opportunities for exploring their fundamental physical properties at the two-dimensional limit. In this work, we propose a unified picture of transport phenomena in the nitrogen-rich 2D W5N6 and Mo5N6, and the stoichiometric 2D δ-MoN based on several observations and first-principles calculations. Temperature coefficient of resistance (TCR) and magnetoresistance (MR) from Hall measurements consistently suggest disorder-induced transport mechanism at low temperatures (10-30 K). Notably, we observe a transition from metal to semimetal driven by the variation of nitrogen content in TMNs, supported by the suppressed density of states at the Fermi energy in nitrogen-rich TMNs (e.g. Mo5N6) from first-principle calculations. Carrier density calculations of bulk TMNs and 2D TMNs with -NH termination groups further reveal the switching of majority carrier type of Mo5N6 at reduced thickness, which is in great agreement with Hall measurement results. Our findings demonstrate that high nitrogen content in metallic molybdenum nitrides can induce the transition to a semimetallic phase at the 2D limit, shedding light on both the fundamental aspects of these materials and directions in future material design.

cond-mat.mtrl-sci↗

arXiv2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/

physics.chem-ph↗

arXiv2026

Tensor Decomposition Networks for Fast Machine Learning Interatomic Potential Computations

$\rm{SO}(3)$-equivariant networks are the dominant models for machine learning interatomic potentials (MLIPs). The key operation of such networks is the Clebsch-Gordan (CG) tensor product, which is computationally expensive. To accelerate the computation, we develop tensor decomposition networks (TDNs) as a class of approximately equivariant networks in which CG tensor products are replaced by low-rank tensor decompositions, such as the CANDECOMP/PARAFAC (CP) decomposition. With the CP decomposition, we prove (i) a uniform bound on the induced error of $\rm{SO}(3)$-equivariance, and (ii) the universality of approximating any equivariant bilinear map. To further reduce the number of parameters, we propose path-weight sharing that ties all multiplicity-space weights across the $\mathcal{O}(L^3)$ CG paths into a single shared parameter set without compromising equivariance, where $L$ is the maximum angular degree. The resulting layer acts as a plug-and-play replacement for tensor products in existing networks, and the computational complexity of tensor products is reduced from $\mathcal{O}(L^6)$ to $\mathcal{O}(L^4)$. We evaluate TDNs on PubChemQCR, a newly curated molecular relaxation dataset containing 105 million DFT-calculated snapshots. We also use existing datasets, including OC20, and OC22. Results show that TDNs achieve competitive performance with dramatic speedup in computations. Our code is publicly available as part of the AIRS library (\href{https://github.com/divelab/AIRS/tree/main/OpenMol/TDN}{https://github.com/divelab/AIRS/}).

cs.LG↗

arXiv2025

Revealing Atomic-Scale Switching Pathways in van der Waals Ferroelectrics

Two-dimensional van der Waals (vdW) materials hold the potential for ultra-scaled ferroelectric (FE) devices due to their silicon compatibility and robust polarization down to atomic scale. However, the inherently weak vdW interactions enable facile sliding between layers, introducing complexities beyond those encountered in conventional ferroelectric materials and presenting significant challenges in uncovering intricate switching pathways. Here, we combine atomic-resolution imaging under in-situ electrical biasing conditions with first-principles calculations to unravel the atomic-scale switching mechanisms in SnSe, a vdW group-IV monochalcogenide. Our results uncover the coexistence of a consecutive 90 degrees switching pathway and a direct 180 degrees switching pathway from antiferroelectric (AFE) to FE order in this vdW system. Atomic-scale investigations and strain analysis reveal that the switching processes simultaneously induce interlayer sliding and compressive strain, while the lattice remains coherent despite the presence of multidomain structures. These findings elucidate vdW ferroelectric switching dynamics at atomic scale and lay the foundation for the rational design of 2D ferroelectric nanodevices.

cond-mat.mtrl-sci↗

arXiv2025

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science.

cs.LG↗

arXiv2025

A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials

Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theory (DFT), provide high accuracy in modeling electronic structures and predicting molecular properties, they are computationally expensive. Machine learning interatomic potentials (MLIPs) have emerged as promising surrogate models that aim to achieve DFT-level accuracy while enabling efficient large-scale atomistic simulations. The development of accurate and transferable MLIPs requires large-scale, high-quality datasets with both energy and force labels. Critically, MLIPs must generalize not only to stable geometries but also to intermediate, non-equilibrium conformations encountered during atomistic simulations. In this work, we introduce PubChemQCR, a large-scale dataset of molecular relaxation trajectories curated from the raw geometry optimization outputs of the PubChemQC project. PubChemQCR is the largest publicly available dataset of DFT-based relaxation trajectories for small organic molecules, comprising approximately 3.5 million trajectories and over 300 million molecular conformations computed at various levels of theory. Each conformation is labeled with both total energy and atomic forces, making the dataset suitable for training and evaluating MLIPs. To provide baselines for future developments, we benchmark nine representative MLIP models on the dataset. Our resources are publicly available at https://huggingface.co/divelab

q-bio.QM↗

arXiv2025

A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures

Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant materials interatomic potential model that integrates both invariant and equivariant message passing layers, while provably satisfying key physical constraints. HIENet achieves state-of-the-art performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet.

cs.LG↗

arXiv2025

Quantum spin Hall effects in van der Waals materials

The quantum spin Hall (QSH) effect, first predicted in graphene by Kane and Mele in 2004, has emerged as a prototypical platform for exploring spin-orbit coupling, topology, and electronic interactions. Initially realized experimentally in quantum wells exhibiting characteristic QSH signatures, the field has since expanded with the discovery of van der Waals (vdW) materials. This review focuses on vdW systems, which offer unique advantages: their exposed surfaces enable a combination of surface-sensitive spectroscopic and microscopic tools for comprehensive detection of the QSH state; mechanical stacking with other vdW layers facilitates symmetry engineering and proximity effects; and moiré engineering introduces layer skyrmion topological phases and strong correlation effects. We highlight two monolayer families, 1T$^\prime$-MX$_2$ and MM$^\prime$X$_4$, represented by WTe$_2$ and TaIrTe$_4$, respectively. These materials exhibit QSH phases intertwined with or in close proximity to other quantum phases, such as excitonic insulators, charge density waves, and superconductivity. Their low crystal symmetry and topology enable rich quantum geometrical responses, ranging from nonlinear Hall effects to circular photogalvanic effects. We also discuss moiré systems, which combine topology with flatband physics and enhanced correlations, driving spontaneous symmetry breaking and transitions from QSH to quantum anomalous Hall (QAH) states. Remarkably, fractionalized QAH and QSH states have recently been observed in moiré systems, significantly advancing the field of condensed matter physics. Finally, we explore emerging applications of QSH and derived materials, such as using nonlinear Hall effects for quantum rectification in microwave energy harvesting and harnessing fractional anomalous states for topological quantum computing.

cond-mat.mes-hall↗

arXiv2025

Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation

We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods.

cs.LG↗

arXiv2024

Two-Fold Anisotropic Superconductivity in Bilayer T$_d$-MoTe$_2$

Noncentrosymmetric 2D superconductors with large spin-orbit coupling offer an opportunity to explore superconducting behaviors far beyond the Pauli limit. One such superconductor, few-layer T$_d$-MoTe$_2$, has large upper critical fields that can exceed the Pauli limit by up to 600%. However, the mechanisms governing this enhancement are still under debate, with theory pointing towards either spin-orbit parity coupling or tilted Ising spin-orbit coupling. Moreover, ferroelectricity concomitant with superconductivity has been recently observed in the bilayer, where strong changes to superconductivity can be observed throughout the ferroelectric transition pathway. Here, we report the superconducting behavior of bilayer T$_d$-MoTe$ _2$ under an in-plane magnetic field, while systematically varying magnetic field angle and out-of-plane electric field strength. We find that superconductivity in bilayer MoTe$_2$ exhibits a two-fold symmetry with an upper critical field maxima occurring along the b-axis and minima along the a-axis. The two-fold rotational symmetry remains robust throughout the entire superconducting region and ferroelectric hysteresis loop. Our experimental observations of the spin-orbit coupling strength (up to 16.4 meV) agree with the spin texture and spin splitting from first-principles calculations, indicating that tilted Ising spin-orbit coupling is the dominant underlying mechanism.

cond-mat.supr-con↗

arXiv2024

A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).

cond-mat.mtrl-sci↗