Search papers, labs, and topics across Lattice.
100 papers published across 9 labs.
LLM4MOF reveals that language-model agents can autonomously and interpretable design complex materials, outperforming conventional search methods at a fraction of the cost.
Amplifying quantum pseudorandom states to t-copy security without extra assumptions opens new avenues for quantum cryptography.
TEPID-ADAPT-VQE achieves chemical accuracy in excited-state simulations while using only one hyperparameter, streamlining the optimization process for quantum chemistry.
Manganese-functionalized GelMA hydrogels could revolutionize precision oncology by enabling real-time MRI-guided immunotherapy with enhanced therapeutic efficacy.
Achieving an order-of-magnitude improvement in force direction prediction, CliffordSTF redefines the capabilities of interatomic potentials.
LLM4MOF reveals that language-model agents can autonomously and interpretable design complex materials, outperforming conventional search methods at a fraction of the cost.
Amplifying quantum pseudorandom states to t-copy security without extra assumptions opens new avenues for quantum cryptography.
TEPID-ADAPT-VQE achieves chemical accuracy in excited-state simulations while using only one hyperparameter, streamlining the optimization process for quantum chemistry.
Manganese-functionalized GelMA hydrogels could revolutionize precision oncology by enabling real-time MRI-guided immunotherapy with enhanced therapeutic efficacy.
Achieving an order-of-magnitude improvement in force direction prediction, CliffordSTF redefines the capabilities of interatomic potentials.
EFT enables LLMs to evolve solutions across diverse optimization tasks, achieving over 10% performance gains and state-of-the-art results in challenging mathematical problems.
A new AI tool can catch 34% more mathematical errors in scientific papers, transforming the peer review landscape.
ENS achieves up to 10x better accuracy than traditional methods in turbulent flow scenarios by directly utilizing the PDE residual as input for iterative error correction.
Learning a truncated Gaussian can be done in optimal time and sample complexity without the usual computational overhead of gradient descent.
Head Fisher alignment can be efficiently estimated in LLMs, revealing critical insights into task similarity that traditional metrics miss.
RecallRisk-BERT outperforms traditional models by integrating text and tabular data, achieving a remarkable accuracy of 0.963 in predicting recall severity.
Achieving 93% accuracy in insect authentication despite batch variations, BISN sets a new standard for robustness in near-infrared spectroscopy.
Transformers can generate complex triangulations that are vital for advancing our understanding of Calabi-Yau manifolds in string theory.
BOBa's innovative bandit-guided approach cuts down computational costs while boosting screening efficiency in the vast chemical space of drug discovery.
Multi-path forecasting reveals that early dynamics can predict divergent future behaviors, enabling more precise interventions in biopharmaceutical production.
L-DPS achieves robust inverse solutions while slashing inference costs, outperforming traditional methods in challenging sparse and noisy environments.
The Extra Trees model achieved an impressive 96.92% accuracy in detecting cirrhosis, highlighting the untapped potential of machine learning in liver disease diagnostics.
Simulation-based inference can achieve rapid and accurate Bayesian calibration of epidemiological models, outperforming traditional MCMC methods in both speed and efficiency.
Time-domain and nonlinear HRV indices are not only stable but also reveal significant gender differences, challenging existing assumptions about their clinical application.
AHOIS not only autonomously discovers new scientific hypotheses but also self-corrects through Socratic questioning, revolutionizing how we approach experimentation in high-dimensional systems.
Continuous modeling of ventricular motion can enhance heart failure risk prediction, outperforming traditional cardiac markers.
An optimal number of thought leaders can enhance team impact, but too many may stifle innovative ideas.
Women in science face a tilted playing field where institutional prestige amplifies success unevenly, favoring men even at lower-ranked institutions.
Achieving logarithmic parallel depth, MergeLLL significantly outperforms classical lattice reduction methods in both efficiency and quality of reduced bases.
Achieving mean deviations of only 0.44 arcminutes from established ephemerides, solarsystem redefines lightweight astronomical calculations.
GenMF achieves a breakthrough in 3D asset fabrication by preserving visual details while reducing stress concentrations, making monochromatic prints more reliable and visually appealing.
Second-order perturbative corrections to LR-SCI yield near-FCI accuracy for static molecular properties, revolutionizing the approach to complex quantum systems.
Achieving chemical accuracy with a search-free ansatz, this method outperforms traditional quantum architecture searches in estimating molecular ground-state energies.
Encapsulating photosystems within a virus-based nanocontainer can dramatically boost electron transfer efficiency, paving the way for greener chemical synthesis.
GRAINS achieves up to 47.8x speedup in genome graph analysis by processing data directly within storage, revolutionizing efficiency in genomic research.
Transformer models not only excel in classifying bacterial Raman spectra but also outperform traditional methods without requiring any preprocessing.
Efficiently training Hamiltonian Neural Networks can now be achieved even under noisy conditions, preserving energy conservation without excessive computational overhead.
Identifiability bounds reveal the precise conditions under which distinct ODEs can be distinguished from solution data, transforming our understanding of equation recovery in scientific machine learning.
Quantum graph neural networks can now achieve scalability and expressivity guarantees, paving the way for practical applications in complex relational data tasks.
The fTNN achieves unprecedented accuracy in solving fractional PDEs, outperforming traditional methods even in challenging scenarios with boundary singularities.
ArBG achieves a remarkable 60% reduction in zero-shot energy error for peptide systems, challenging the dominance of flow-based sampling methods.
Mixing rates improve by 1.5-3 times with the new SA-PAL method, showcasing a dramatic leap in sampling efficiency for complex systems.
KANs show promise in aerodynamic prediction but struggle with stability and hyperparameter sensitivity compared to MLPs and GNNs.
A novel meta-optimization framework that evolves evaluation criteria can dramatically accelerate scientific discovery, achieving a 67x speedup in algorithm performance.
LLMs excel at factual recall but falter on quantitative reasoning and conceptual tasks, revealing critical gaps in their domain-specific capabilities.
Achieving over 51% peak efficiency in a compact, wideband Doherty power amplifier through innovative deep learning techniques could revolutionize power amplifier design.
LLMs are reshaping the complexity of scientific language, evidenced by increased word turnover and altered relationships between style and complexity metrics.
Formulaic expression desensitization not only boosts dataset scale but also enhances model generalization in extracting key sentences from scientific literature.
NeuMatEx outperforms PBR techniques by extracting complex neural materials with unprecedented visual fidelity and precision from multi-view images.
A novel metamaterial enables real-time, ultrasensitive detection of molecular transformations by combining SEIRA and SERS, revolutionizing chemical fingerprinting.
Achieving noise-floor accuracy in self-limiting saturation curve modeling with just seven measurements could revolutionize experimental design in fields like pharmacology and materials science.
HI-NQS achieves chemical accuracy on strongly correlated systems with a scaling advantage over traditional methods, all without quantum computing resources.
Grounding EEG interpretations in hardware capabilities can drastically reduce unsupported conclusions and improve the reliability of scientific software.
Heavy-tailed estimators in Variational Monte Carlo can undermine convergence, but a new clipping method shows promise for robust optimization in electronic structure calculations.
Medical records and cardiac biomarkers alone outperform vascular graphs in predicting pulmonary embolism risk, raising questions about the value of complex vascular representations.
OncoSynth slashes treatment effect estimation errors by up to 66% in oncology, transforming how synthetic data can inform precision medicine.
Misclassifying medical conditions can have severe consequences, and the Ordinal Cross-Entropy framework significantly reduces prediction errors by respecting the ordinal nature of disease severity.
Achieving precise EUV mask designs through a physics-informed neural operator could revolutionize semiconductor manufacturing processes.
Hyperbolic neural quantum states outperform their Euclidean counterparts at critical points, revealing a new frontier in many-body quantum physics modeling.
Blasto-Net achieves remarkable accuracy in blastocyst analysis, outperforming traditional methods while providing interpretable results that can directly influence IVF outcomes.
Achieving 96.06% accuracy in laser welding penetration prediction with only 200 labeled images could revolutionize quality assurance in industrial applications.
AI-assisted workflows can cut down experiment reproduction efforts by up to six times, but struggle with complex analysis tasks requiring human oversight.
Moderately difficult research in NLP achieves greater academic impact, revealing a critical balance for researchers to target.
A dynamic load balancer can reduce node idle time to nearly a millisecond in complex UQ workflows, revolutionizing how we approach scheduling in high-performance computing.
Achieving high accuracy in diradical electronic structure calculations with up to five orders of magnitude less computational time than traditional methods could revolutionize the design of next-gen optoelectronic materials.
The mechanical critical pressure of RHO zeolites can be tuned over an order of magnitude simply by adjusting the isoreticular index, revealing a universal property of this nanoporous family.
Microalgae can boost CO2 conversion efficiency by up to 10 times, offering a groundbreaking shift in photocatalytic strategies.
Molexar achieves 100% validity in molecular generation while outperforming larger models, making it a game-changer for efficient drug design.
A new algorithm simplifies the search for bound states in coupled-channel calculations, enhancing computational efficiency and accuracy.
Active tuberculosis can spontaneously emerge in a Mars colony from latent infections, highlighting critical health risks for future space missions.
Quantum back-action can dynamically create excitonic states, challenging the conventional understanding of excitonic behavior in semiconductors.
TensorLDM achieves near-ground-truth diffusion tensor reconstruction with a remarkable SPD-violation rate of just 1.54%, setting a new standard for anatomical consistency in DTI.
Cross-attention models can predict imatinib response in GISTs with remarkable internal accuracy but struggle with external validation, revealing the challenges of generalizing multimodal insights.
Achieving a Strehl ratio of 0.96 with just 2 to 4 iterations could revolutionize adaptive optics calibration in high-power laser applications.
Achieving optimal solutions for engineering design problems with a distributed quantum simulator that rivals classical methods in efficiency and accuracy.
Achieving over 2x speedup in DNA variant calling could revolutionize genomic analysis workflows and accelerate discoveries in complex disease research.
Evolving hardware-aware compression techniques can outperform human designs, achieving unprecedented efficiency in deploying massive AI models.
A unified categorical framework reveals that each level of chemical description captures unique insights, transforming our understanding of reaction networks and their stability.
Inflated fluid-solid vesicles transition from simple cylindrical shapes to intricate crumples and wrinkles, revealing a surprising dependence on curvature resistance.
SPAs can deliver consistent quantum chemistry results with classical efficiency, rivaling traditional methods like Hartree-Fock.
Achieving comparable CO2 capture productivity to high-temperature systems while dramatically lowering energy requirements and enabling water management could revolutionize direct air capture technology.
JetFormer outperforms other tokenization methods in reconstruction quality, but VQ-VAE shines in predicting galaxy physical properties, revealing critical trade-offs in scientific data representation.
SP-Mind achieves state-of-the-art performance in spatial proteomics analysis by autonomously converting natural-language queries into comprehensive analytical workflows.
Neural network techniques can effectively solve complex systems of ergodic BSDEs, revealing how regime switches dramatically alter forward utility preferences.
Extracting critical phase-field model parameters from just one snapshot could revolutionize how we approach materials modeling in dynamic systems.
Ariadne achieves a 31.2-point increase in solvability for constrained retrosynthesis while cutting computation time from hours to minutes.
Achieving unprecedented accuracy in reconstructing localized flow structures, this model outperforms traditional methods in real-world fluid dynamics scenarios.
The Hartley Neural Operator outperforms its Fourier counterpart for elliptic PDEs by leveraging real-valued representations, challenging the dominance of complex methods in neural operator design.
M-dwarf exoplanets could host robust forms of oxygenic photosynthesis, producing NIR biosignatures that challenge our Earth-centric views on habitability.
DeepBD outperforms traditional variant prioritization tools by integrating LLMs and specialized evidence modules, achieving a Recall@10 of 92.9% on a large cohort of genetic cases.
Probabilistic forecasting of Alzheimer's progression reveals that uncertainty grows significantly over time, especially for patients with rare progression patterns.
MVG-KAN achieves superior PM$_{2.5}$ forecasting by leveraging a novel Geo-Wind Graph that captures wind-driven pollutant transport dynamics.
RetiSEM achieves superior causal accuracy in fragmented biomedical data, revealing hidden indirect effects that traditional methods miss.
A novel benchmark reveals that integrating physics-based modeling with Bayesian methods can drastically enhance the reliability of monitoring offshore wind turbine structures from limited data.
A variational autoencoder significantly boosts the robustness of neural network-based model reduction for turbulent flow predictions, ensuring high accuracy even in complex vehicle geometries.
As peer reviews progress, positive sentiment rises significantly, challenging the notion that critique intensifies with scrutiny. WHY_IT MATTERS: This insight could transform how we understand the peer review process, potentially leading to improved evaluation strategies in scientific publishing.
Agon reveals that machine-driven research can scale effectively while exposing critical failure modes that still require human oversight.
Classic algorithms maintain their dominance in influence, but their decline reveals a predictable loss of network centrality that could inform future research trajectories.
Dynamic interval encoding in QUBO-based CT reconstruction significantly enhances image fidelity, outperforming traditional methods in challenging scenarios.
S1-Omni-Image not only generates high-quality scientific images but also understands and edits them with unprecedented precision, outperforming existing models across several key benchmarks.
Achieving a 9x speedup in gyrokinetic simulations could revolutionize the efficiency of plasma physics research.
Cavity coupling can dramatically alter molecular response properties, revealing hidden frequencies where no cavity effects occur.
Autocatalytic behavior can be quantitatively analyzed in complex chemical networks using a novel scale-splitting algorithm that simplifies reaction pathways and dynamics.
Causal relationships among principal components in protein dynamics reveal hidden influences that traditional analyses overlook.
Local charge inversion in mesopores can induce giant, periodic ionic current oscillations, with implications for neuromorphic computing and ionic circuits.
