Scientific Discovery & Drug Design

MLLMs exhibit significant weaknesses in following scientific instructions, especially in chemistry, where they fail to meet fine-grained constraints despite increased model size.

Uncovering a rich atlas of physical concepts in a neutrino model reveals that interpretability can dramatically enhance angular resolution in particle physics tasks.

A neural-operator surrogate enables rapid inversion of millions of airborne electromagnetic soundings, achieving near real-time results previously deemed infeasible.

Fine-tuning a single molecular foundation model can yield superior performance across multiple olfactory tasks, challenging the need for task-specific models.

VINCENT achieves a remarkable mean motif recall of 0.826, significantly outperforming existing models in identifying key molecular regions driving drug synergy.

Predictive accuracy and uncertainty quantification improve significantly when using a row-wise PCA approach that respects physical constraints in multi-output Gaussian processes.

FlowMoDL achieves unprecedented accuracy in 4D flow MRI reconstruction, outperforming traditional and contemporary models even under extreme acceleration conditions.

A novel deep-learning framework uncovers 97 promising solid-state electrolytes, revealing that Li$^{+}$ jump-network connectivity is the key to high ionic conductivity.

Orthogonal projection reveals that protein language model embeddings capture critical biochemical features, with their removal leading to significant drops in predictive performance.

Achieving up to 49.4% faster computations for neutron diffusion problems without sacrificing accuracy could revolutionize reactor core analysis.

A generative model can slash the computational cost of simulating stochastic chemical reactions while preserving accuracy.

Low temperature continental climates deliver the highest discounted lifetime energy yield for photovoltaic modules, reshaping strategies for solar energy deployment.

Task-oriented sampling strategies can substantially elevate multimodal protein language model performance, revealing that default protocols are often inadequate.

LLMs misjudge research ideas as "medium novel" due to a systematic bias, but a new probing method boosts their accuracy by over 22%.

Evidence weighting in Homo-RAG boosts retrieval effectiveness, achieving near-perfect relevance scores while enhancing the quality of gene function predictions for non-model organisms.

Core-valence double ionization reveals that core holes dramatically alter molecular orbital configurations, challenging existing assumptions about electronic structure in SF6.

A new AI-integrated curriculum for mechanical engineering empowers students to tackle complex thermal engineering problems while enhancing their interdisciplinary skills.

Recovery rates of lithium and cobalt can be significantly impacted by the presence of H$_2$O$_2$ and the formation of passivating films during leaching processes.

Non-Dyson ADC methods may introduce significant errors in ionization potential calculations, challenging their reliability in correlated electron theories.

Achieving 97% accuracy in reconstructing complex cryo-EM mixtures could revolutionize how we analyze heterogeneous biological samples.

Enzymatic degradation of plastics may leave behind "fluffy" microparticle clusters that significantly impact recycling efficiency.

PAGS achieves sharper reconstructions in photoacoustic tomography by eliminating the need for calibrated speed-of-sound priors, even in heterogeneous media.

Delayed observations can dramatically enhance El Niño forecasts, but more complex models don't necessarily lead to better predictions.

oFM not only outperforms traditional prognostic models but also links treatment outcomes to biological mechanisms, revolutionizing patient care in oncology.

BioKERN achieves superior biological-neighborhood retrieval by leveraging an explicit biological kernel, outperforming existing methods without relying on larger models.

Quantum kernels can significantly boost fraud detection accuracy by aligning feature-map geometry with interaction-sensitive decision boundaries, outperforming traditional methods.

Calibration of uncertainty in structural monitoring can be dramatically improved, reducing coverage error by nearly 56% across diverse operational conditions.

Identifying distinct disease trajectories in T2DM reveals critical insights into patient progression and comorbidity risk that could transform clinical management.

Classical algorithms are fundamentally outmatched by quantum methods in sampling from Gibbs states, requiring exponentially more queries as dimensions increase.

Neural emulators can outperform traditional PDE solvers by leveraging insights from both paradigms, revealing a surprising synergy that enhances simulation efficiency.

Deep learning can sift through imaging noise to reveal thousands of potential dual active galactic nuclei previously dismissed as false positives.

Integrating physics directly into neural operators can cut prediction errors by up to 2.2 times, transforming how we model complex dynamical systems.

Conditional memory can either enhance or hinder scientific reasoning, and knowing when to activate it is crucial for optimal performance.

A mesh-free deep energy method achieves near-perfect accuracy in brittle fracture modeling, outperforming traditional approaches on complex crack patterns.

EMFE achieves 94.6% accuracy in malaria cell classification while remaining computationally efficient and interpretable, challenging the dominance of deep learning in this space.

Transforming a conical intersection into an avoided crossing reveals that topology can drastically alter molecular dynamics, leading to distinct hopping rates even on identical energy surfaces.

Soot concentration in equi-diffusive flames increases with pressure, but soot production rates are exclusively pressure-dependent, revealing a nuanced relationship that challenges existing combustion models.

Achieving DFT-level precision in NMR shielding calculations with a scalable, efficient framework could revolutionize how we interpret NMR spectra across diverse chemical systems.

Photoexcitation of gold nanoclusters triggers a rapid and complex reorganization of their ligand interfaces, revealing new pathways for energy transfer and catalysis.

A novel minimal chemical oscillator reveals a kink-like discontinuity in thermodynamic quantities at the Hopf bifurcation, challenging previous numerical-only insights.

High partial scores in scientific workflows can be misleading, as models often claim completion without delivering results, revealing a critical flaw in current evaluation methods.

AI usage in mathematical research surged from 1.39% to 14.09% of submissions in just five months, with Combinatorics leading the charge.

A novel self-consistent field framework reveals that finite quantum-chemical clusters can effectively model polarization and charge transfer under constant potential boundary conditions.

NEGF-MCPDFT reveals that carbon wire junctions exhibit transport properties that closely match experimental observations, challenging existing models of electron correlation.

Best-effort computing can achieve a 92% scaling efficiency in evolutionary models while gracefully handling hardware failures—transforming our approach to high-performance digital evolution.

The ability to design RNA sequences with short helices opens new avenues for RNA engineering, challenging previous limitations in structural RNA design.

Weak Raman bands can arise from strong atomic contributions canceling each other out, challenging traditional interpretations of Raman intensity.

Altering the reordering rate can change nucleation rates by several orders of magnitude, revealing a surprising turnover effect in multi-step nucleation processes.

Collective intermolecular Coulombic decay can occur in gases, enabling multiple donors to ionize an acceptor even with insufficient individual energy.

3D conformational fluctuations can significantly enhance polymer chain rupture rates, but bending stiffness may reverse this effect, leading to unexpected dependencies on bond position.