arXiv subjects

Xiaolong Zou

Publications and source records attributed to Xiaolong Zou.

At least 19 recordsLinked to original sources

arXiv2026

Differentiable OPLS Force Field Parameterization for Ionic Electrolytes and High-Throughput Application to Lithium-ion Batteries

The rational design of ionic electrolytes for lithium-ion batteries (LIBs) is severely constrained by the vast solvent-salt combinatorial space and low efficiency of empirical trial-and-error. While molecular dynamics (MD) bridges microscopic solvation structures and macroscopic physicochemical properties, classical force fields often lack sufficient accuracy for multicomponent systems. To address these challenges, we develop an automated differentiable OPLS-AA force field parameterization workflow tailored for general ionic electrolytes. It employs topology-guided atom typification to reduce parameter redundancy and optimizes Lennard-Jones parameters via the DMFF framework, with experimental density as the fitting target and ionic conductivity as an independent validation metric. Rigorous convergence tests yield a standardized simulation protocol with $\sim$100,000-atom systems and 35-40 ns NVT runs to ensure reliable transport property quantification. High-throughput MD simulations of over 10,000 formulations spanning 67 solvents and 15 lithium salts are conducted on the Tianqiong platform, generating a comprehensive dataset covering five core properties: density, dielectric constant, viscosity, diffusion coefficient, and ionic conductivity. t-SNE visualization reveals partial clustering of distinct salt chemistries, continuous property gradients with concentration and temperature, and internal physical self-consistency, with solvent composition identified as another key performance regulator. Together, the accurate transferable force field and large-scale dataset provide a solid foundation for data-driven rational design of ionic electrolytes.

physics.chem-ph↗

arXiv2026

Spin-Valley-Mismatched Altermagnet for Giant Tunneling Magnetoresistance

Altermagnet-based heterojunctions have demonstrated magnetoresistive effects in experiments, however, a predictive theoretical model for non-ferromagnetic structures has remained elusive. In this work, we develop a tunneling-based spin-transport theory that explicitly incorporates the transverse-wavevector ($\bf{k}_\|$)-dependent spin polarization of an altermagnet's transport channels, enabling the prediction of giant tunneling magnetoresistance (TMR). Based on the theory, we predict that the altermagnet KV$_2$Se$_2$O can reach the extreme limit of magnetoresistance. By performing first-principles transport calculations, we verify that magnetic tunnel junctions using the metallic KV$_2$Se$_2$O as the electrodes and few-layer MgO as the spacer exhibit zero-bias magnetoresistance larger than $7.57\times10^7$\%, which is robust against the bias and thickness of the spacer. Our research provides a quantitative design principle for next-generation spin-electronic devices and establishes KV$_2$Se$_2$O/MgO/KV$_2$Se$_2$O as a leading candidate material system for room-temperature ultra-high-density non-volatile memory.

cond-mat.mtrl-sci↗

arXiv2026

Symmetry-guided and AI-accelerated design of intercalated transition metal dichalcogenides for antiferromagnetic spintronics

The advancement of antiferromagnetic spintronics depends on quantum materials with target symmetry-dictated functionalities, however, their systematic discovery is hindered by the immense configurational complexity of the available material space. Here, we introduce a symmetry-guided, AI-accelerated framework incorporating graph neural networks with high generalization ability to overcome this bottleneck. Based on fully intercalated transition metal dichalcogenides (iTMDs) and using only 200 relaxed partially intercalated structures for transfer learning, our model effectively explores more than 100,000 partially intercalated configurations and identifies 35 altermagnetic and 20 $Tτ$-antiferromagnetic ground-state candidates. Interestingly, we show that tuning spin-group symmetry through intercalant arrangement or magnetic ordering realizes a series of d-wave altermagnets in these hexagonal systems with high spin-charge conversion efficiency. Furthermore, we reveal plentiful $Tτ$-antiferromagnets enabling efficient Néel spin-orbit torque switching, driven by giant $T$-odd spin Edelstein susceptibilities. These results establish iTMDs as a versatile platform for spintronics and provide a general strategy for the accelerated design of symmetry-enforced quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

Giant tunneling magnetoresistance based on spin-valley-mismatched ferromagnetic metals

Half metals, which are amenable to perfect spin filtering, can be utilized for high-magnetoresistive devices. However, available half metals are very limited. Here, we demonstrate that materials with intrinsic spin-valley-mismatched (SVM) states can be used to block charge transport, resembling half metals and leading to giant tunneling magnetoresistance. As an example, by using first-principles transport calculations, we show that ferromagnetic 1\emph{T}-VSe$_2$, 1\emph{T}-VS$_2$, and 2\emph{H}-VS$_2$ are such spin-valley-mismatched metals, and giant magnetoresistance of more than 99\% can be realized in spin-valve van der Waals (vdW) junctions using these metals as electrodes. Owing to the intrinsic mismatch of spin states, the central-layer materials for the vdW junctions can be arbitrary nonmagnetic materials, in principle. Our research provides clear physical insights into the mechanism for high magnetoresistance and opens new avenues for the search and design of high-magnetoresistance devices.

cond-mat.mtrl-sci↗

arXiv2025

Efficient and Robust Spatial-to-Fiber Coupling forMultimode Quantum Networks via CascadedAdaptive Feedback Control

Duan-Lukin-Cirac-Zoller (DLCZ)-based multimodequantum networks rely on efficient spatial-to-fiber coupling, yetenvironmental perturbations compromise this performance. Wedevelop a cascaded adaptive feedback control system integratedinto the quantum entanglement source preparation path.Leveraging a power-feedback hillclimbing algorithm, itdynamically regulates piezoelectric-actuated mirrors to achieveautonomous multi-dimensional beam alignment, Experimentsshow it rapidly boosts single-mode fiber (SMF) coupling efficieneyto over 70% within 20 seconds and entering the most efficient andstable transmission state after 75 seconds.Importantly, it enhancesthe stability of the atom-photon interfacecritical for quantumlight-matter interactionsproviding a practical framework forefficient, robust spatial light transmission in scalable quantumnetworks.

quant-ph↗

arXiv2025

Band-spin-valley coupled exciton physics in antiferromagnetic MnPS$_3$

The introduction of intrinsic magnetic order in two-dimensional (2D) semiconductors offers great opportunities for investigating correlated excitonic phenomena. Here, we employ full-spinor GW plus Bethe-Salpeter equation methodology to reveal rich exciton physics in a prototypical 2D Néel-type antiferromagnetic semiconductor MnPS$_3$, enabled by the interplay among inverted dispersion of the second valence band, spin-valley coupling and magnetic order. The negative hole mass increases the reduced mass of the lowest-energy bright exciton, leading to exchange splitting enhancement of the bright exciton relative to band-edge dark exciton. Notably, such splitting couples with spontaneous valley polarization to generate distinct excitonic fine structure between $K$ and $-K$ valleys, which dictate distinct relaxation behaviors. Crucially, magnetic order transition from Néel antiferromagnetic to ferromagnetic state induces significant quasiparticle band structure reconstruction and excitonic transitions modification, with low-energy optical excitations being exclusively contributed by majority-spin channel. These findings establish 2D antiferromagnetic semiconductors as an intriguing platform to study band-spin-valley coupled exciton physics.

cond-mat.mtrl-sci↗

arXiv2025

Exclusive Generation of Single-Atom Sulfur for Ultrahigh Quality Monolayer MoS$_2$ Growth

Preparation of high-quality two-dimensional (2D) transition metal dichalcogenides (TMDCs) is the precondition for realizing their applications. However, the synthesized 2D TMDCs (e.g., MoS$_2$) crystals suffer from low quality due to the massive defects formed during the growth. Here, we report the single-atom sulfur (S1) as a highly reactive sulfur species to grow ultrahigh-quality monolayer MoS$_2$. Derived from battery waste, the sulfurized polyacrylonitrile (SPAN) is found to be exclusive and efficient in releasing S1. The monolayer MoS$_2$ prepared by SPAN exhibits an ultralow defect density of $~7\times 10^{12}$ cm$^{-2}$ and the narrowest photoluminescence (PL) emission peak with full-width at half-maximum of ~47.11 meV at room temperature. Moreover, the statistical resonance Raman and low-temperature PL results further verify the significantly lower defect density and higher optical quality of SPAN-grown MoS$_2$ than the conventional S-powder-grown samples. This work provides an effective approach for preparing ultrahigh-quality 2D single crystals, facilitating their industrial applications.

cond-mat.mtrl-sci↗

arXiv2025

Cu Intercalation-stabilized 1T'-MoS2 with Electrical Insulating Behavior

The intercalated two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted much attention for their designable structure and novel properties. Among this family, host materials with low symmetry such as 1T' phase TMDCs are particularly interesting because of their potentials in inducing unconventional phenomena. However, such systems typically have low quality and poor stability, hindering further study in the structure-property relationship and applications. In this work, we intercalated Cu into 1T' MoS2 with high crystallinity and high thermal stability up to ~300 oC. We identified the distribution and arrangement of Cu intercalators for the first time, and the results show that Cu occupy partial of the tetrahedral interstices aligned with Mo sites. The obtained Cu-1T' MoS2 exhibits an insulating hopping transport behavior with a large temperature coefficient of resistance reaching -4 ~ -2 % K-1. This work broadens the artificial intercalated structure library and promotes structure design and property modulation of layered materials.

cond-mat.mtrl-sci↗

arXiv2024

Surprising pressure-induced magnetic transformations from Helimagnetic order to Antiferromagnetic state in NiI2

Interlayer magnetic interactions play a pivotal role in determining the magnetic arrangement within van der Waals (vdW) magnets, and the remarkable tunability of these interactions through applied pressure further enhances their significance. Here, we investigate NiI2 flakes, a representative vdW magnet, under hydrostatic pressures up to 11 GPa. We reveal a notable increase in magnetic transition temperatures for both helimagnetic and antiferromagnetic states, and find that a reversible transition from helimagnetic to antiferromagnetic (AFM) phases at approximately 7 GPa challenges established theoretical and experimental expectations. While the increase in transition temperature aligns with pressure-enhanced overall exchange interaction strengths, we identify the significant role of the second-nearest neighbor interlayer interaction, which competes with intra-layer frustration and favors the AFM state as demonstrated in the Monte Carlo simulations. Experimental and simulated results converge on the existence of an intermediate helimagnetic ordered state in NiI2 before transitioning to the AFM state. These findings underscore the pivotal role of interlayer interactions in shaping the magnetic ground state, providing fresh perspectives for innovative applications in nanoscale magnetic device design.

cond-mat.mtrl-sci↗

arXiv2023

DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction

Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with finer supervision strategies have been applied to boost and guide the relaxation process of an atomic system and better predict its properties at the equilibrium state. In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. The method enhances the supervision signal, reduces the multiplicity of solutions in edge representation, and encourages the model to provide node predictions that are graph structural variation robust. DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm. The DR-Label strategy was applied to three radically distinct models, each of which displayed consistent performance enhancements. Based on the DR-Label strategy, we further proposed DRFormer, which achieved a new state-of-the-art performance on the Open Catalyst 2020 (OC20) dataset and the Cu-based single-atom-alloyed CO adsorption (SAA) dataset. We expect that our work will highlight crucial steps for the development of a more accurate model in equilibrium state property prediction of a catalysis system.

cs.LG↗

arXiv2023

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.

q-bio.NC↗

arXiv2022

Probing complex stacking in a layered material via electron-nuclear quadrupolar coupling

For layered materials, the interlayer stacking is a critical degree of freedom tuning electronic properties, while its microscopic characterization faces great challenges. The transition-metal dichalcogenide 1T-TaS$_2$ represents a novel example, in which the stacking pattern is not only enriched by the spontaneous occurrence of the intralayer charge density wave, but also recognized as a key to understand the nature of the low-temperature insulating phase. We exploit the $^{33}\rm{S}$ nuclei in a 1T-TaS$_2$ single crystal as sensitive probes of the local stacking pattern via quadrupolar coupling to the electron density distribution nearby, by combining nuclear magnetic resonance (NMR) measurements with the state-of-the-art first-principles electric-field gradient calculations. The applicability of our proposal is analyzed through temperature, magnetic-field, and angle dependent NMR spectra. Systematic simulations of a single 1T-TaS$_2$ layer, bilayers with different stacking patterns, and typical stacking orders in three-dimensional (3D) structures unravel distinct NMR characteristics. Particularly, one 3D structure achieves a quantitative agreement with the experimental spectrum, which clearly rationalizes the coexistence of two types of interfacial environments. Our method may find general applications in the studies of layered materials.

cond-mat.mtrl-sci↗

arXiv2022

Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction

Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as input, few GNN models can predict the thermodynamic properties of relaxed configurations with an acceptable level of error. In this work, we develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task. Taking CO adsorption on Cu-based single-atom alloy catalysts as an illustration, we show that our method can reliably estimate CO adsorption energy with a mean absolute error of 0.087 eV from the initial CO adsorption structures without costly first-principles calculations. Further, compared to other state-of-the-art GNN methods, our model exhibits improved generalization ability when predicting catalytic performance of out-of-domain configurations, built with either unseen substrate surfaces or doping species. We show that the proposed MT GNN strategy can facilitate catalyst discovery.

cond-mat.mtrl-sci↗

arXiv2022

Highly Efficient and Selective Extraction of Gold by Reduced Graphene Oxide

Materials that are capable of extracting gold from complex sources, especially electronic waste (e-waste) with high efficiency are needed for gold resource sustainability and effective e-waste recycling. However, it remains challenging to achieve high extraction capacity to trace amount of gold, and precise selectivity to gold over a wide range of complex co-existing elements. Here we report a reduced graphene oxide (rGO) material that has an ultrahigh extraction capacity for trace amounts of gold (1,850 mg/g and 1,180 mg/g to 10 ppm and 1 ppm gold). The excellent gold extraction behavior is accounted to the graphene areas and oxidized regions of rGO. The graphene areas spontaneously reduce gold ions to metallic gold, and the oxidized regions provide a good dispersibility so that efficient adsorption and reduction of gold ions by the graphene area can be realized. The rGO is also highly selective to gold ions. By controlling the protonation process of the functional groups on the oxidized regions of rGO, it shows an exclusive gold extraction without adsorption of 14 co-existing elements seen in e-waste. These discoveries are further exploited in highly efficient, continuous gold recycling from e-waste with good scalability and economic viability, as exemplified by extracting gold from e-waste using a rGO membrane based flow-through process.

cond-mat.mtrl-sci↗

arXiv2022

A Ta-TaS2 monolithic catalyst with robust and metallic interface for superior hydrogen evolution

The use of highly active and robust catalysts is crucial for producing green hydrogen by water electrolysis as we strive to achieve global carbon neutrality. Noble metals like platinum are currently used in industry for the hydrogen evolution reaction (HER), but suffer from scarcity, high price and unsatisfied performance and stability at large current density, restricting their large scale implementations. Here we report the synthesis of a new type of monolithic catalyst (MC) consisting of a metal disulfide (e.g., TaS2) catalyst vertically bonded to a conductive substrate of the same metal by strong covalent bonds. These features give the MC a mechanically robust and electrically near zero resistance interface, leading to an outstanding HER performance including rapid charge transfer and excellent durability, together with a low overpotential of 398 mV to achieve a current density of 2,000 mA cm-2 as required by industry. The Ta TaS2 MC has a negligible performance decay after 200 h operation at large current densities. In light of its unique interface and the various choice of metal elements giving the same structure, such monolithic materials may have broad uses besides catalysis.

cond-mat.mtrl-sci↗

arXiv2021

Layer-controlled Ferromagnetism in Atomically Thin CrSiTe$_3$ Flakes

The research on two-dimensional (2D) van der Waals (vdW) ferromagnets has promoted the development of ultrahigh-density and nanoscale data storage. However, intrinsic ferromagnetism in layered magnets is always subject to many factors, such as stacking orders, interlayer couplings, and the number of layers. Here, we report a magnetic transition from soft to hard ferromagnetic behaviors as the thickness of CrSiTe$_3$ flakes decreases down to several nanometers. Phenomenally, in contrast to the negligible hysteresis loop in the bulk counterparts, atomically thin CrSiTe$_3$ shows a rectangular loop with finite magnetization and coercivity as thickness decreases down to ~8 nm, indicative of a single-domain and out-of-plane ferromagnetic order. We find that the stray field is weakened with decreasing thickness, which suppresses the formation of the domain wall. In addition, thickness-dependent ferromagnetic properties also reveal a crossover from 3 dimensional to 2 dimensional Ising ferromagnets at a ~7 nm thickness of CrSiTe$_3$, accompanied by a drop of the Curie temperature from 33 K for bulk to ~17 K for 4 nm sample.

cond-mat.mtrl-sci↗

arXiv2021

Understanding the flat band in 1T-TaS2 using a rotated basis

Electronic flat bands serve as a unique platform to achieve strongly-correlated phases. The emergence of a flat band around the Fermi level in 1T-TaS$_2$ in accompany with the development of a $\sqrt{13}\times\sqrt{13}$ charge density wave (CDW) superlattice has long been noticed experimentally, but a transparent theoretical understanding remains elusive. We show that without CDW, the primary feature of the $1\times1$ bands can be fitted by a simple trigonometric function, and physically understood by choosing a rotated $\tilde{t}_{2g}$ basis with the principle axes aligning to the tilted TaS$_6$ octahedron. Using this basis, we trace the band evolution in the $\sqrt{13}\times\sqrt{13}$ superlattice by progressively including different CDW effects. We point out that CDW strongly rehybridizes the three $\tilde{t}_{2g}$ orbitals, which leads to the formation of a well-localized molecular orbital and spawns the flat band.

cond-mat.mtrl-sci↗

arXiv2021

Glue-Assisted Grinding Exfoliation of Large-Size 2D Materials for Insulating Thermal Conduction and Large-Current-Density Hydrogen Evolution

Two-dimensional (2D) materials have many promising applications, but their scalable production remains challenging. Herein, we develop a glue-assisted grinding exfoliation (GAGE) method in which the adhesive polymer acts as a glue to massively produce 2D materials with large lateral sizes, high quality, and high yield. Density functional theory simulation shows that the exfoliation mechanism involves the competition between the binding energy of selected polymers and the 2D materials which is larger than the exfoliation energy of the layered materials. Taking h-BN as an example, the GAGE produces 2D h-BN with an average lateral size of 2.18 μm and thickness of 3.91 nm. The method is also extended to produce various other 2D materials, including graphene, MoS2, Bi2O2Se, vermiculite, and montmorillonite. Two representative applications of thus-produced 2D materials have been demonstrated, including h-BN/polymer composites for insulating thermal conduction and MoS2 electrocatalysts for large-current-density hydrogen evolution, indicating the great potential of massively produced 2D materials.

physics.app-ph↗