NeurIPS 2025PastAI for science
NeurIPS 2025 AI for Science Workshop
NeurIPS2025-AI4Science
- Submission deadline
- Aug 28, 2025, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (230)
Fetched from OpenReview (v2) on 2026-06-10.
10 Million Particle Events: Enabling Foundation Models for Sparse 3D Inverse Problems
A Foundational Dataset for the Predictive Prevention of Waterborne Disease
A Large Multimodal Molecular Representation Encoder-Decoder Foundation Model for Chemistry
A Multi-Modal Deep Learning Model for Drug Potency Prediction: Leveraging Features from Physics-Based Docking and Advanced Co-Folding Methods
A Probabilistic U-Net Approach to Downscaling Climate Simulations
A study of EHVI vs fixed scalarization for molecule design
A Synthesizability-Guided Pipeline for Materials Discovery
AC-PKAN: Attention-Enhanced and Chebyshev Polynomial-Based Physics-Informed Kolmogorov–Arnold Networks
Accelerated Isotopologue Reduced Partition Function Ratio Prediction with Orbital-based Deep Learning
Accelerating Protein Molecular Dynamics Simulation with DeepJump
Adaptive Transition State Refinement with Learned Equilibrium Flows
AI for Science Strategic Compass: Aligning Discovery Tensions with Core AI Functions
AI4O3: A Foundational Data Collection for Artificial Intelligence in Tropospheric Ozone Research
AIM: Adaptive Intervention for Deep Multi-task Learning of Molecular Properties
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Alvessa: An Agentic Evidence-Grounded Research Assistant for Genomics
An Agentic Orchestration System for Heliophysics Tasks
An in-silico integration of neurodevelopmental and dopaminergic views of schizophrenia
Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling
Augmenting Research Ideation with Data: An Empirical Investigation in Social Science
AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up
Automated scientific minimization of regret for cognitive modeling
BasePrompt: Self-Prompting Genome Language Models for RNA Fitness Prediction
Benchmarking LLMs for atomic-level geometric manipulation in crystals
Benchmarking Machine Learning Potentials for Crystal Structure Relaxation
Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning
Beyond data subsampling: differentiation as an uncertainty source in equation discovery
Beyond Ensembles: Simulating All-Atom Protein Dynamics in a Learned Latent Space
Beyond model organisms: robust prediction of functional properties across protein evolution
Bigger is not always better: evaluating target-specific dataset design strategies for regioselectivity prediction on complex molecules
BioMedReasoner: Towards Multi-Hop Reasoning using Path-based Relational Learning on Biomedical Knowledge Graphs
BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation
Block-wise distillation for lightweight weather models
BLOSUM Is All You Learn — Generative Antibody Models Reflect Evolutionary Priors
Boundary-Augmented Neural Operators for Better Generalization to Unseen Geometries
Bridging Neural Operator and Flow Matching for a Generative PDE Foundation Model
CALM-PDE: Continuous and Adaptive Convolutions for Latent Space Modeling of Time-dependent PDEs
Can Theoretical Physics Research Benefit from Language Agents?
CAST: Causal Modeling of Time-Varying Treatment Effects on Head and Neck Cancer
Causal AI Scientist: Facilitating Causal Data Science with Large Language Models
Chemist-aligned retrosynthesis by ensembling diverse inductive bias models
CHEMSETS: How Capable Are Chemistry LLMs?
CiteGuard: Retrieval-Augmented Citation Verification for LLM-Powered Peer Review
Closing the Omics Gap: A Benchmark for Unified Evaluation of Biomolecular Foundation Models
CompGen: A Conditional Generation Framework for Inverse Composition Design of Catalytic Surfaces
Conditioned Clifford-Steerable Kernels
Connecting Preclinical Models to Patient Outcomes: A Machine Learning Dataset for Predictive Validity in Drug Development
Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
Constant-Potential Machine Learning Force Field for Electrochemical Interface
Constructing the Mental Health Phenome: An Open Multimodal Dataset Linking Digital Behavior, Physical Health, and Mental Wellbeing
Control-Augmented Diffusion for Autoregressive Data Assimilation
Data-Dependent Smoothing for Protein Discovery with Walk-Jump Sampling
Data-driven Design as a High-Impact, Ecologically Valid Benchmark for Document Understanding
Data-Driven Solar Surface Flux Transport Modeling with Uncertainty Quantification
Data-optimal scaling of paired antibody language models
De novo generation of functional terpene synthases using TpsGPT
Decompose, Adapt, and Evolve: Towards Efficient Scientific Equation Discovery with Large Language Models
Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact
Demystifying Protein Generation with Hierarchical Conditional Diffusion Models
Differentiable Predictive Control for Precise Oxygen Level Maintenance for Critical Patients
Diffusion for Fusion: Designing Stellarators with Generative AI
Dimensionality and Topological Stability of Neural Representations in the Human Brain Predict Learning Outcomes
DINO: dynamics-informed dataset to overcome the limitations of static molecular data in AI-driven drug discovery
Discontinuous Epitope Fragments as Sufficient Target Templates for Efficient Binder Design
Dissecting Larval Zebrafish Hunting Behavior using Deep Reinforcement Learning trained RNNs
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Diverse Topology Optimization using Modulated Neural Fields
DMPKBench: A Multi-Modal Benchmark for Evaluating LLMs and Agents in Drug Discovery DMPK Tasks
DMRG Quantum Chemistry Dataset for Multi-Reference Machine Learning
Do Llamas Understand the Periodic Table?
Does LLM dream of differential equation discovery?
Domain-Invariant Feature Learning for Patient-Level Phenotype Prediction from Single-Cell Data
EARS-UDE : Evaluating Auditory Response in Sensory Overload with Universal Differential Equations
Einstein Fields: A Neural Perspective To Computational General Relativity
Emergent SO(3)-Invariant Molecular Representations from Multimodal Alignment
Empowering AI in RNAi Therapeutics: A Foundational Dataset for siRNA Design and Optimization
EquiHGNN: Scalable Rotationally Equivariant Hypergraph Neural Networks
Every Answer Counts: Enhancing Scientific Discovery with Efficient Entity-Centric Question Answering from Long Contexts
Explainable AI–Guided Virtual Experiments Reveal How DNA Sequence Context Shapes Gene Regulation
Explaining Temporal Effects in Sepsis Prediction
Exploring Generative Approaches for Predicting Copolymer Sequences from Reaction Conditions
FALCON: An ML Framework for Fully Automated Layout-Constrained Analog Circuit Design
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
First Comprehensive Benchmark for Tailored Small Molecule-Binding Aptamer Design
Foundation Models Enabling Multi-Scale Battery Materials Discovery: From Molecules To Devices
From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation
From Molecules to Perception: A Benchmark Dataset for AI in Sensory Science
From Static Structures to Ensembles: Studying and Harnessing Protein Structure Tokenization
GAPMAP: Mapping Scientific Knowledge Gaps in Biomedical Literature Using Large Language Models
GCP-VQVAE: A Geometry-Complete Language for Protein 3D Structure
Generalization Beyond Benchmarks: Evaluating Learnable Protein-Ligand Scoring Functions on Unseen Targets
Generative AI Enables Medical Image Segmentation in Ultra Low-Data Regimes
Generative Latent Space Dynamics of Electron Density
GeoGraph: Geometric and Graph-based Ensemble Descriptors for Intrinsically Disordered Proteins
Geometry Aware Inference of Steady State PDEs Using Equivariant Neural Field Representations
Gradient-Free Physics-informed Operator Learning using Walk-on-Spheres
Graph Neural Networks for Interferometer Simulations
Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization
Holonic Science: A New Framework for Benchmarking AI Scientists
How knowledge discovery and embedded paradigm transform industrial process management: exploring pipeline hydraulic dynamic identification
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
IM-LPG: Inverse Modeling Approach to Laser Pulse Shape Generation in Inertial Confinement Fusion
Improved Therapeutic Antibody Reformatting through Multimodal Machine Learning
Improving RNA Secondary Structure Prediction Through Expanded Training Data
Is Sequence Information All You Need for Bayesian Optimization of Antibodies?
Label-free biochemical imaging of neural organoids via deep learning-enhanced Raman microspectroscopy
Large-scale audio-language datasets for bioacoustics
LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping
Learning Boltzmann Generators via Constrained Mass Transport
Learning chaotic PDEs with boundedness guarantees
Learning Deformable Body Interactions With Adaptive Spatial Tokenization
Learning Protein-Ligand Binding in Hyperbolic Space
Learning to Compress Plasma Turbulence
LEONARDO: A Physics-Informed Generative Model for Stochastic Nanoparticle Dynamics in Liquid-Phase TEM
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design
LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
LLM Kernel: an evaluation framework for open-ended scientific interpretation
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
MARSHA: Multi-Agent RAG System for Hazard Adaptation
Measuring Dependencies between Biological Signals with Self-supervision, and its Limitations
Mechanistic Reaction Data for Interpretable Deep Learning in Chemistry
MEGA: A Large-Scale Molecular Editing Dataset for Guided-Action Optimization
Memory-Augmented Reinforcement Learning for Hierarchical Graph Optimization of Dynamic Bills of Materials in Sustainable Medical device Product Families
MetaOmics-10T: The Foundational Dataset to Unlock Causal Modeling of Microbial Ecosystems
Mixture-of-Experts Guided Multi-Omic Integration for Gastrointestinal Cancer Subtype Prediction
MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Mol-SGCL: Molecular Substructure-Guided Contrastive Learning for Out-of-Distribution Generalization
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery \\via Hierarchical Search
MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback
moPPIt-v3: Motif-Specific Peptides Generated via Multi-Objective-Guided Discrete Flow Matching
MORGaN: self-supervised multi-relational graph learning for drug target discovery
MSAFlow: a Unified Approach for MSA Representation, Augmentation, and Family-based Protein Design
Multi-Graph Meta-Transformer: An Interpretable Framework for Cross-Graph Functional Alignment in Neural Decoding
Multi-Modal Attention Framework for Underwater Bioacoustic Denoising and Recognition
Multi-Objective Nanobody Design via Masked Discrete Diffusion with Simplex Refinement
Multi-Objective Peptide Design via Token-Aligned Preference Optimization
Multi-Scale Classification of Green Bank Telescope Signals
Multilevel neural simulation-based inference
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning
Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
Neural network distillation of orbital dependent density functional theory
Neural Triangular Transport Maps: A New Approach Towards Sampling in Lattice QCD
OmniCast: A Masked Latent Diffusion Model for Weather Forecasting Across Time Scales
OpenCityCorpus: A Large-Scale, Harmonized, and LLM-Ready Corpus of Urban Data for Scientific Research
OpenDiscovery: A Verifiable, Creative Science Problem-Solving Dataset to Forge AI Scientists
Pareto-Guided Reinforcement Learning for Multi-Objective ADMET Optimization in Generative Drug Design
PatchDNA: A Flexible and Biologically-Informed Alternative to Tokenization for DNA
PEAR: Equal Area Weather Forecasting on the Sphere
PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research
PhySense: Evaluating LLMs on Foundational Physics Principles
Physics-Informed Learning Near Critical Transitions: A Comparative Study of UDEs and Neural ODEs
Physics-Informed Neural Networks with Fourier Features and Attention-Driven Decoding
PhysiX: A Foundation Model for Physics Simulations
PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training
PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models
PKG-DPO: Optimizing Domain-Specific AI systems with Physics Knowledge Graphs and Direct Preference Optimization
PLAME: Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Predicting Kinase-Specific Phosphorylation Sites with Pretrained Protein Language Models
Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
PrimerCast: Predictive Modeling of PCR Amplification with an AI-Ready Experimental Dataset
Proposal for a Large-scale High-quality Dataset of Activity Cliffs
Protein Design with Agent Rosetta: A Case Study for Specialized Scientific Agents
PUBHOMICS: A Multispecies Biological Dataset to Catalyze AI-Driven Toxicity Assessment for Environmental and Public Health
RAG-Enhanced Collaborative LLM Agents for Drug Discovery
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
ReactionReasoner: Towards Reasoning LLM for Chemical Reaction Prediction
README: Rapid Equation Discovery with Multimodel Encoders
Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling
RemoteFoldSet: Benchmarking Structural Awareness of Protein Language Models
Resilience Outcomes Benchmark: Toward an Outcome-Labeled Coping Strategy Dataset for Precision Mental Health
Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation
Revive Legacy Scientific Reasoning Benchmark by Growing Perturbation
RNA-Scope: Benchmarking RNA Language Models for RNA Sequence Understanding
Rodent-Bench
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
Sampling 3D Molecular Conformers with Diffusion Transformers
Scaling High-Throughput Experimentation Unlocks Robust Reaction-Outcome Prediction
Scaling Multi-Modal and Multi-Task Transformers for Small Molecule Drug Discovery
scCMap: Connecting Genetic and Chemical Perturbations at Single-Cell Resolution
Scientific Machine Learning for Symbolic Recovery of Relativistic Effects in Black Hole Orbits
SciKnowEval: A Comprehensive Dataset for Evaluating Scientific Knowledge of Large Language Models
SciNav: A General Agent Framework for Scientific Coding Tasks
Semantic search for 100M+ galaxy images using AI-generated captions
Sinhala Diachronic Corpus
SkillPuzzler: A Self-Evolving Agentic Framework for Materials and Chemistry Research with Minimal Reliance on Predefined Tools
Smiles2Dock: a large-scale dataset for ML-based docking score prediction using AlphaFold structures
Softly Constrained Denoisers for Diffusion Models
SPADE: Inferring Transcriptional Dynamics from Spatial Transcriptomics with Physics-Informed Deep Learning
Sparse Autoencoders for Low-$N$ Protein Function Prediction and Design
Sparse Mixture-of-Experts for Multi-Channel Imaging: Are All Channel Interactions Required?
Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection
Static and Dynamic Diffusion Emulators: From Sampling Gray Swan Extreme Events to Suffering from Model Collapse
Steering the Evolutionary Game: Hierarchical Control of Therapeutic Resistance in Cancer Treatment
Steering Vector Fields for Property-Controlled Molecular Generation with Chemical Language Models
Synergizing Large Language Models and Knowledge Graphs in Science: A Survey
SynthFair: A Semi-Synthetic Medical Imaging Dataset to Propel Research on Bias Detection & Mitigation
TadABench-1M: A Large-Scale Wet-Lab Protein Benchmark For Rigorous OOD Evaluation
Task Alignment Outweighs Framework Choice in Scientific LLM Agents
TeBaAb: Text-Based Antigen-Conditioned Antibody Redesign via Directed Evolution
Test-Time Control Over Accuracy-Cost Trade-Offs in Neural Physics Simulators via Recurrent Depth
The Darwin–Gödel Discovery Machine: Toward Bounded-Risk Self-Improving AI4Science
The Loss Landscape of XRD-Based Structure Optimization Is Too Rough for Gradient Descent
The More You Automate, the Less You See: The Hidden Pitfalls of AI Scientist Systems
The Transparent Earth: A Multimodal Foundation Model for the Earth's Subsurface
Thinking like a CHEMIST: Combined Heterogeneous Embedding Model Integrating Structure and Tokens
Token-Level Early Fusion Model Bridging Text and 3D Electron Density Grids in Chemistry
Token-Level Guided Discrete Diffusion for Membrane Protein Design
Topological defects propagate information in deep neural networks
Topological Feature Compression for Molecular Graph Neural Networks
Topological Graph Generative Model for Ecological Design
TorchQuantumDistributed
Towards Accurate Test-Time Adaptation for Neural Surrogates
Towards Generating Stable Materials via Large Language Models with Reinforcement Learning Finetuning
Towards Multi-Fidelity Scaling Laws of Neural Surrogates in CFD
Training Dynamics of Learning 3D-Rotational Equivariance
TroubleRAG: Evaluating Retrieval Pipelines for Real-World Chemistry Troubleshooting
Trustworthy Retrosynthesis: Eliminating Hallucinations with a Diverse Ensemble of Reaction Scorers
Unlearning as Ablation: Toward a Falsifiable Benchmark for Generative Scientific Discovery
Urban Climate Counterfactuals: A Causal Dataset for Street-Level Heat Mitigation Interventions
Using Deep Reinforcement Learning to Understand Odor Plume Tracking in Walking and Flying Agents
WhaleLM: Finding Structure and Information in Sperm Whale Vocalizations and Behavior with Machine Learning
When Do LLMs Improve Bayesian Optimization? A Systematic Comparison Across Molecular and Protein Design
WildSci: Advancing Scientific Reasoning from In-the-Wild Literature
Without Safeguards, AI-Biology Integration Risks Accelerating Future Pandemics
Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
Zephyrus: An Agentic Framework for Weather Science
Zero-Shot Protein–Ligand Binding-Residue Prediction from Sequence and SMILES