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Ju Li

Publications and source records attributed to Ju Li.

At least 19 recordsLinked to original sources
arXiv2026

Hyperfine spectroscopy and nuclear spin control of dark defects in diamond

Spin defects in diamond serve as powerful building blocks for quantum technologies, especially for quantum sensing and networking. Electron-nuclear defects formed near optically active spins such as the nitrogen-vacancy center provide a resource for multi-qubit quantum registers. However, many remain uncharacterized, limiting their control and integration in quantum devices. Here, we identify the hyperfine components, including their relative sign, and nuclear spin species of two unknown dark spin defects using zero-field double electron-electron resonance spectroscopy and a protocol based on electron-nuclear double resonance. New ab initio calculations assign one defect to a known nitrogen interstitial, while the hydrogen-related defect matches no calculated or previously reported structure. We further use this protocol to demonstrate initialization, unitary control, and long-lived coherence of the hydrogen nuclear spin qubit, with a coherence time of 1.0(3) ms. These characterization and control tools establish a framework for harnessing unknown electron-nuclear defects for quantum registers.

quant-ph↗
arXiv2026

LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale

Machine learning interatomic potentials (MLIPs) bridge the gap between quantum-mechanical accuracy and scalable molecular simulations. However, a fundamental trade-off persists: universal MLIPs (u-MLIPs) offer broad transferability but are computationally expensive, whereas task-specific MLIPs (ts-MLIPs) achieve high efficiency at the expense of costly DFT training data. Here we introduce LightPFP, a data-efficient knowledge distillation framework. Instead of relying on expensive DFT calculations, LightPFP constructs a distilled ts-MLIP by using a u-MLIP to generate high-quality, material-specific training data, while employing a pre-trained lightweight model to further improve data efficiency. Across diverse material systems, LightPFP reduces model development time by approximately three orders of magnitude compared with DFT-based workflows, while maintaining DFT-level accuracy. Combined with a LAMMPS/Kokkos inference backend, LightPFP achieves more than three orders of magnitude higher inference efficiency than u-MLIP baselines while enabling simulations of systems thousands of times larger. When the teacher exhibits systematic bias, LightPFP supports few-shot precision correction using as few as 10 high-accuracy DFT configurations, as demonstrated by reproducing MgO's experimental melting point. This u-MLIP-driven distillation establishes a scalable route to high-fidelity, data-efficient MLIPs, accelerating the pace of materials discovery and design.

cond-mat.mtrl-sci↗
arXiv2026

VerifiedLab: Harnessing Knowns for Unknowns

Across human history, knowledge has undergone several transitions, from orality to writing and then to digital archives. At each transition, humans established new conventions to preserve knowledge for the new era. Nowadays, as research agents are increasingly deployed for laboratory experiments, knowledge is crossing another boundary: from records written for humans to those for machines. In this work, we introduce VerifiedLab, a multi-agent harness framework for this human-to-machine transition of knowledge. VerifiedLab transforms research output from passive artifacts into active systems that accelerate downstream use, adoption and prediction of future trends. In VerifiedLab, a team of AI scientists with distinct roles reasons over records verified field by field: a principal investigator plans, a librarian computes the evidence directly from the records, specialists draft, and a gate and a critic check each claim against the measured reliability of its data before the harness decides whether to release or withhold it. We demonstrate the efficacy of VerifiedLab through extensive experiments on perovskite solar cells: VerifiedLab recovers known physical trends, anticipates devices reported a year later, and lets AI scientists inherit the literature together with a measure of how far it can be trusted. By scaling the inference-time verification compute, VerifiedLab introduces a new pathway for knowledge dissemination and machine harness for AI co-scientists.

cs.AI↗
arXiv2026

Beyond the Point Mass: Acceptance-Derived Weights and Distributional Losses for Speculative Decoding

In speculative decoding, a lightweight draft model proposes tokens that the target model verifies in parallel, speeding up generation without changing the target distribution. D-PACE (Wu et al., 2026) trains such drafters with a loss that weights each position by its contribution to the expected accepted length. Both its weights and its cross-entropy (CE) loss use only the draft's probability of the recorded target token, treating the target as a point mass. Under rejection sampling, however, a sampled draft token is accepted with probability equal to the overlap of the draft and target distributions, one minus their total variation (TV). We derive D-PAL (Dynamic Position-wise Acceptance Loss) from the exact expected accepted length, using this overlap as the acceptance signal. This yields overlap-based position weights with log acceptance (LA) as the per-position loss, and the objective reduces to D-PACE for a point-mass target. Since the weights are detached, any per-position loss can replace LA in practice. We show that forward KL divergence and Rényi divergence of order 1/2 match or exceed LA, and that Rényi-1/2 provably bounds the true acceptance rate from above and below. Replacing CE with Rényi-1/2 improves acceptance length under both D-PACE's weights and ours. On six benchmarks with a Qwen3-4B target, D-PAL raises mean acceptance length over D-PACE from 4.50 to 4.57 at T=0 and from 4.20 to 4.28 at T=1, and consistently achieves higher throughput at both temperatures.

cs.LG↗
arXiv2026

Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions

How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to act, exchange payments, and accumulate wealth from environmental rewards. These simple local incentives induce decentralized credit assignment, driving global planning without explicit orchestration or communication protocols. The population evolves through economic selection: effective agents accumulate wealth and are mutated via exploitation, while ineffective ones go bankrupt and are replaced via exploration. Using modern Large Language Model (LLM) based agents, we show that, initialized with weak agents, the agent economy produces emergent multi-step reasoning strategies and outperforms stronger monolithic baselines across five agentic tasks, including mathematical reasoning, financial research, scientific research, accelerator design, and distributed-system optimization. Our results suggest a new path to multi-agent intelligence: rather than engineering coordination, we can design decentralized incentive structures under which it automatically emerges.

cs.CL↗
arXiv2026

Cooperative Quantum Optical Effects of Moiré Exciton Superlattices

The unique properties of two-dimensional moiré systems have been widely studied from many perspectives. However, relatively little work has investigated how the real-space structure of moiré systems can directly engender novel properties and functionalities. In this Letter, we explore how intriguing cooperative optical effects can emerge from the real-space superlattice formed by moiré excitons. Particularly, we show that the collective moiré exciton states can have either strongly enhanced (superradiant) or suppressed (subradiant) radiative decay rates, depending on their in-plane wave vector. These super- and subradiant states can be efficiently switched by a gate-induced electric field gradient. Moreover, the cooperative transmittance T of the atomically thin moiré system can be switched from $T\approx 0$ (opaque) to $T \approx 1$ (transparent) with $\lesssim ~1\%$ heterostrain or a $1^{\circ}$ adjustment in the twist angle $θ$. These features are robust against nonradiative losses and disorders, making the moiré system a highly versatile platform for cooperative quantum optics with potential applications in, e.g., single photon storage and switching.

cond-mat.mtrl-sci↗
arXiv2026

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, HARP (Hamiltonian Read-out for Properties), that predicts an effective one-electron Hamiltonian from one inexpensive B3LYP/def2-SVP calculation and derives a broad suite of properties from it (energy, optical gap, dipole, quadrupole, polarizability, Mulliken atomic charges, and Mayer bond orders) at coupled-cluster accuracy across nine main-group elements, including the under-served phosphorus, sulfur, and chlorine chemistries. The model is trained on a new in-house dataset of multi-property labels computed at the CCSD(T) level for all nine elements. On a held-out test set, it reduces the error of every property by a factor of 3.8 to 270 relative to semi-local, hybrid, and double-hybrid DFT (referenced to composite CCSD(T)/cc-pVTZ), while adding only ~0.1 s wall time per molecule, delivering coupled-cluster-quality predictions at the cost of a single DFT calculation. Critically, deriving every property from a predicted Hamiltonian rather than pooling per-atom features builds the correct size-scaling into the model architecture: on pi-conjugated oligothiophenes it matches finite-field CCSD polarizability to ~1% and the EOM-CCSD optical gap to ~3% at the largest sizes where those references remain affordable (44 and 37 atoms, where a single CCSD field point already costs ~500x the model's entire inference) and extrapolates the corrected trends to 58-atom chains, a regime where pooling-based architectures fail by construction. Accurate extrapolation is therefore set by the model's inductive bias rather than by the training data.

physics.chem-ph↗
arXiv2026

Dual Role of Mobile Interstitials in Defect Kinetics: From Retardation to Acceleration

Mobile interstitial atoms redistribute while defects migrate, complicating prediction of defect kinetics and leading to contradictory reports of acceleration and retardation. We formulate defect activation in a grand-canonical ensemble at fixed interstitial chemical potential and define a pathway free-energy landscape $ΔG(Λ;T,μ)$ and the corresponding activation free energy $ΔG^{\ddagger}$. We compute these quantities using both hyperplane-constrained thermodynamic integration via a mean force relation and a two-state chemical-potential integration, denoted TSCPI. The latter requires sampling only the initial and transition states; a single chemical-potential integration then yields $ΔG^{\ddagger}(T,μ)$ across a wide $μ$ range, enabling rapid mapping over temperature and chemical potential. Applied to vacancy diffusion in FCC, BCC, and HCP metals with H (including in plane and cross plane diffusion in Zr) and in BCC W with He, both free energy routes agree with diffusion coefficients from molecular dynamics mean square displacements. The resulting maps reveal regimes of suppression and enhancement, including a crossover from suppression to enhancement with increasing hydrogen concentration. A site occupancy analysis links barrier shifts to state dependent site spectrum changes and transferable interstitial interaction terms.

cond-mat.mtrl-sci↗
arXiv2026

Encapsulation epitaxy of air-stable monolayer superconducting films for quantum circuits and qubits

Two-dimensional (2D) superconductors are an emerging platform for strongly correlated physics and quantum information science. Their reduced dimensionality, atomically flat interfaces, and high crystallinity are attractive for realizing compact lumped-element devices in superconducting circuits. However, synthesizing large-area, monolayer 2D superconductors remains challenging because of their susceptibility to oxidation. Here, we report an "encapsulation epitaxy" mechanism that enables the growth of large-area, air-stable, monolayer superconducting NbSe2 films and explore their use in superconducting quantum circuits. A 2D encapsulation layer, such as graphene or hexagonal boron nitride (hBN), pre-deposited on a 3D substrate (e.g., SiO2 or Si3N4), serves both as a template for epitaxial growth of monolayer NbSe2 (1L-NbSe2) underneath it and as a protective cover. This approach produces uniform, large-area (>1-inch) 1L-NbSe2 with greatly enhanced ambient stability, enabling device fabrication in air. The resulting 1L-graphene/NbSe2 heterostructures exhibit robust superconductivity (Tc ~ 1 K) and enhanced charge density wave order (TCDW ~ 177 K), indicative of high material quality. We further integrate 1L-NbSe2 into superconducting circuits using oxidation-free transfer and superconducting edge-contact techniques. The 1L-NbSe2 exhibits a measured kinetic inductance LK ~ 0.7 nH/square, making it suitable for quantum circuits requiring high-kinetic-inductance elements. Encapsulation epitaxy thus provides a route to air-stable 2D superconductors and van der Waals heterostructures, with potential for wafer-scale, monolithic fabrication of superconducting quantum circuitry.

cond-mat.supr-con↗
arXiv2026

Nonresonant optomechanical control of structural phases

Optical tweezers demonstrate how light can exert forces to trap, repel, and manipulate microscopic particles without absorption. Recent theory has suggested that such forces can extend beyond particle manipulation to drive structural phase transitions in solids. Here we apply this optomechanical principle to tin selenide (SnSe), a material where proximity to several different structural phases gives rise to its high thermoelectric figure of merit and makes it a candidate for a switchable topological crystalline insulator. Whereas the force for standard optical tweezers arises from a gradient in the intensity of a light field, the optomechanical force is mediated by a gradient in the dielectric constant as a function of phonon coordinate. Unlike conventional methods that rely on resonant excitation and absorption through the imaginary part of the dielectric function, this approach operates dispersively through the real part and can be directly driven by Raman processes, enabling selective transitions with reduced energy cost and ultrafast response. Using time-domain Raman scattering, we show that above a critical mid-infrared field strength the $A_g$ Raman modes disappear abruptly without softening, signaling the formation of a new structural phase. This phase, distinct from those induced by heating or carrier excitation, exhibits large-amplitude and long-lived modulations in its optical response. Complementing this observation, we show also evidence for an equivalent DC-field-driven structural phase transformation to a higher symmetry phase, as observed by atom probe tomography. Our study demonstrates the concept of nonresonant optomechanical phase control and defines novel opportunities for synthesizing hidden structural phases with unique functional properties.

physics.optics↗
arXiv2026

Atomistic Language Models Understand and Generate Materials

Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-tuned on lossy textual encodings that discard atomistic information. We introduce Atomistic Language Models (ALMs) to pursue native multimodality, in which a single language backbone understands atomistic structures, generates materials from natural language, and optimizes crystal structures as instructed by text. By unifying a pretrained atomistic encoder, large language model, and denoising diffusion model through purely continuous projectors and staged training, ALMs achieve state-of-the-art results on crystal structure prediction and de novo generation. ALMs are enabled by a continuous bridge that maps language model embeddings directly into the steering space of atomistic diffusion, and are assisted by Text-to-Crystal Feynman-Kac (T2C-FK), a particle-based sampler that scores partial denoising trajectories to enforce stoichiometric targets at inference time. To evaluate the ability of ALMs to optimize and generate materials from natural-language prompts and 3D atom-coordinate inputs, we introduce ALM Bench, the first benchmark for text-conditioned crystal generation and optimization. Code, training data, and model weights will be released soon.

cs.LG↗
arXiv2026

Complementary Thermodynamic Mechanisms of Boron and Carbon Segregation at Grain Boundaries in Nickel Alloys

Grain boundary stabilization by light interstitials is central to the performance of Ni-based superalloys, yet the thermodynamic mechanisms governing their interactions with substitutional chemistry remain poorly resolved. Here, we use hybrid Monte Carlo molecular dynamics simulations to quantify how boron and carbon modify the thermodynamic, structural, and chemical ordering of grain boundaries in Ni--Cr alloys. By analyzing interfacial state variables, site-resolved segregation spectra, local chemical ordering, and structural evolution, we show that boron and carbon stabilize grain boundaries through complementary pathways. Carbon drives saturation-controlled stabilization by recruiting Cr and conditioning boundary chemistry, while suppressing temperature-driven structural transformations of the boundary. In contrast, boron stabilizes grain boundaries through a selective mechanism that lowers the interfacial grand potential via localized ordering while permitting gradual structural evolution. These effects arise from coupled interactions between interstitial segregation and Cr redistribution, which together regulate site accessibility, chemical competition, and the range of accessible interfacial states. This work provides a thermodynamic framework for grain boundary engineering and suggests design principles for leveraging interstitial-substitutional interactions in alloys.

cond-mat.mtrl-sci↗
arXiv2026

Can LLMs extract scientific consensus? A case study in high-temperature superconductivity

Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. While large language models (LLMs) have shown impressive performance in information extraction and summarization, their ability to recover latent scientific consensus remains unclear. Here, we investigate this problem in the context of high-temperature superconductivity (HTS), a long-standing and highly debated topic in condensed matter physics, as a challenging testbed. Using near 18,000 highly-cited publications over the past seven decades, we construct a structured knowledge graph linking competing superconducting mechanisms, material families, evidential modalities, and citation relations. We find that LLM-extracted representations recover coherent and physically interpretable structures, including family-dependent mechanism profiles, evidence-specific correlations, and citation-mediated temporal evolution of scientific beliefs. Ablation studies on LLM further show that the global structure remains robust across prompting, decoding, and model variations. Our results suggest that LLMs can indeed serve as scalable tools for deciphering scientific knowledge in domains characterized by competing interpretations and evolving knowledge.

cs.DL↗
arXiv2026

Harnessing AtomisticSkills for Agentic Atomistic Research

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists.

physics.chem-ph↗
arXiv2026

D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting

Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffusion-based parallel drafters such as DFlash predict the full B-token block in one forward pass, enabling deeper drafters and longer accepted blocks. However, existing multi-token drafter objectives often use fixed position-dependent weighting schedules, such as head-dependent weights or block-position decays, which do not adapt as the positions limiting acceptance change during training. To address this, we derive per-position training weights from a differentiable surrogate of expected accepted draft length, matching the weight of each position to its log-probability gradient contribution. The resulting loss, D-PACE (Dynamic Position-Aware Cross-Entropy), shifts training signal toward positions that currently limit acceptance as the drafter improves. Across six benchmarks, two Qwen3-4B draft depths, two decoding temperatures, and two additional target models, D-PACE consistently improves both wall-clock speedup and average emitted length, with 2.3\% measured training-time overhead and no changes to the drafter architecture or inference procedure.

cs.LG↗
arXiv2026

Multi-Persona Debate System for Automated Scientific Hypothesis Generation

Modern scientific discovery is bottlenecked not by data scarcity, but by the inability to synthesize fragmented knowledge into actionable hypotheses. This challenge is especially acute in battery materials research, where electrochemical performance, interfacial behavior, and manufacturing feasibility must be optimized simultaneously. Here, we present the Multi-Persona Debate System (MPDS), a literature-grounded framework for automated scientific hypothesis generation that combines literature retrieval, long-context large language model reasoning, corpus-driven persona induction, and structured multi-agent debate. MPDS constructs literature snapshots of up to 500 papers, grounds agents in role-specific evidence pools, and conducts a three-round citation-aware debate followed by moderator synthesis, enabling negotiation between personas while preserving evidence traceability. We evaluate MPDS using a temporally controlled protocol excluding direct access to target papers, including two held-out battery-materials case studies and a blinded comparison across 30 matched cases. In sodium-ion anode and all-solid-state battery cathode design tasks, MPDS recovered design logics aligned with experimentally validated solution spaces and generated more mechanistically explicit, process-aware proposals than simpler baselines. To assess the impact of personas and debate, we introduce Integrative Hypothesis Quality scoring. In ablation studies, MPDS achieved the highest mean score among five conditions, with its largest advantage in cross-perspective integration. A laboratory follow-up suggests utility as a diagnostic aid for identifying practical bottlenecks in workflows. These results indicate that structured debate over literature snapshots improves hypothesis formation under coupled engineering constraints and provides a reusable workflow for text-intensive scientific discovery.

cs.CL↗
arXiv2026

Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms

Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicomponent systems remains a significant challenge. Here, we propose an improvement of genetic algorithm (GA) -based CSP method using a universal NNP. Our GA-based methods are designed to efficiently expand convex hull volumes while preserving the diversity of crystal structures. Our hull-informed filtering and elitist-selection procedures incorporate an aging mechanism that prioritizes recently improved compositions. We also employ niching to prevent convergence to a small set of stoichiometries, thereby preserving a diverse, high-quality population. Our evaluation shows that the present method outperforms the symmetry-aware random structure generation and existing CSP methods, achieving a larger convex hull with fewer trials. We demonstrated that our approach, combined with the developed universal NNP (PFP), can accurately reproduce and explore phase diagrams obtained through DFT calculations; this indicates the validity of PFP across a wide range of crystal structures and element combinations. This study, which integrates a universal NNP with a GA-based CSP method, highlights the promise of these methods in materials discovery.

cond-mat.mtrl-sci↗
arXiv2026

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional

Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional theory (DFT). However, most existing uMLIPs are trained on Perdew--Burke--Ernzerhof (PBE) generalized gradient approximation (GGA) data and are therefore fundamentally limited by PBE-level accuracy. In this paper, we argue that better zero-shot predictions versus experiments must be an explicit design target for uMLIPs and present PFP v8, a uMLIP available on the Matlantis service that overcomes the inherent limitations of the PBE functional by being trained to reproduce the regularized-restored strongly constrained and appropriately normed (r2SCAN) meta-GGA potential-energy surface across a wide range of chemical domains. Without requiring domain-specific fine-tuning, PFP v8 delivers systematically improved agreement with experimental data or high-accuracy references for crystals, molecules, and surfaces, outperforming PBE-based DFT calculations. Crucially, in long-time molecular dynamics simulations that are computationally impractical with DFT, PFP v8 predicts melting points with an average error of approximately 130 K, halving the error relative to PBE-trained models. These results establish that uMLIPs can move beyond the limitations of their training approximations and achieve substantially improved agreement with experiment across diverse chemical domains, further narrowing the gap between simulation and reality.

physics.chem-ph↗