ICML 2024PastAI for science
ICML 2024 AI for Science Workshop
ICML2024-AI4Science
- Submission deadline
- May 26, 2024, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (156)
Fetched from OpenReview (v2) on 2026-06-10.
3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
A Multi-View Mixture-of-Experts based on Language and Graphs for Molecular Properties Prediction
A Neural Material Point Method for Particle-based Simulations
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Accelerating statistical inferences in astrophysics with Neural Networks and Hamiltonian Monte Carlo
Accounting for Selection Effects in Supernova Cosmology with Simulation-Based Inference and Hierarchical Bayesian Modelling
Active propulsion noise shaping for multi-rotor aircraft localization
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields
AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction
AstroPT: Scaling Large Observation Models for Astronomy
Bayesian Optimization for the Discovery of Redox Active Quinones
Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation
Boost Your Crystal Model with Denoising Pre-training
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
Cell Morphology-Guided Small Molecule Generation with GFlowNets
Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks
CodonMPNN for Organism Specific and Codon Optimal Inverse Folding
Consistent Validation for Predictive Methods in Spatial Settings
Constructing gauge-invariant neural networks for scientific applications
Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport
Decoding Chemical Predictions: Group Contribution Methods for XAI
Deep Learning for Protein-Ligand Docking: Are We There Yet?
Diagnosing and fixing common problems in Bayesian optimization for molecule design
DiffusionPDE: Generative PDE-Solving Under Partial Observation
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling
EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer
Efficiency and Transferability of Inductive Mondrian Conformal Predictors for Drug-Drug Synergy
Efficient 3D Molecular Generation with Flow Matching and Scale Optimal Transport
Efficient Evolutionary Search over Chemical Space with Large Language Models
EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
Energy-Free Guidance of Geometric Diffusion Models for 3D Molecule Inverse Design
Enhancing Peak Assignment in CNMR Spectroscopy: A Novel Approach Using Multimodal Alignment
Enhancing Protein Design Robustness through Noise-Informed Sequence Design
Ensemble Guidance: Towards Generative 3D SBDD in Bioactive Chemical Spaces
Equation identification for fluid flows via physics-informed neural networks
EquiTorch: A Modularized Package for Flexibly Constructing Equivariant GNNs Building upon Pytorch-Geometric
Equivariant Neural Diffusion for Molecule Generation
Equivariant Transformer Forcefields for Molecular Conformer Generation
Euler operators for mis-specified physics-informed neural networks
Exploration and Application of AI in Space Science
Exploring Neural Scaling Laws in Molecular Pretraining with Synthetic Tasks
Fast-forward FARGO: Accelerating Protoplanetary Disk Simulations with Limited Data
Filling in the Gaps: LLM-Based Structured Data Generation from Semi-Structured Scientific Data
Flexible Docking via Unbalanced Flow Matching
Forecasting Smog Clouds With Deep Learning: A Proof-Of-Concept
Fourier Neural Operator based surrogates for $\textrm{CO}_2$ storage in realistic geologies
FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
Graph Multi-Similarity Learning for Molecular Property Prediction
GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
Grappa - A Machine Learned Molecular Mechanics Force Field
Hyperspectral Unmixing for Raman Spectroscopy via Physics-Constrained Autoencoders
Impact4Cast: Forecasting high-impact research topics via machine learning on evolving knowledge graphs
Improving AlphaFlow for Efficient Protein Ensembles Generation
Improving the Accuracy of Coarse-grained Partial Differential Equations with Grid-based Reinforcement Learning
Inpainting crystal structure generations with score-based denoising
Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics
Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations
Knowledge Graph Extraction from Total Synthesis Documents
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Large-Scale Discovery of Experimental Designs in Super-Resolution Microscopy with XLuminA
Learning cure kinetics of frontal polymerization PDEs using differentiable simulations
Learning Long Timescale in Molecular Dynamics by Nano-GPT
Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks
Many-Shot In-Context Learning for Molecular Inverse Design
Marrying Causal Representation Learning with Dynamical Systems for Science
Masking in Molecular Graphs Leveraging Reaction Context
MESS: Modern Electronic Structure Simulations
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
Meta-Designing Quantum Experiments with Language Models
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets
Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI
Modeling Droplets Dynamics in Emulsions with Graph Neural Networks
MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
Multi-Frequency Progressive Refinement for Learned Inverse Scattering
Multi-task Extension of Geometrically Aligned Transfer Encoder
Navigating Chemical Space with Latent Flows
NCIDiff: Non-covalent Interaction-generative Diffusion Model for Improving Reliability of 3D Molecule Generation Inside Protein Pocket
NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries
Neural Incremental Data Assimilation
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
Non-Differentiable Diffusion Guidance for Improved Molecular Geometry
On the Effectiveness of Quantum Chemistry Pre-training for Pharmacological Property Prediction
Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking
PAIR: Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations
Parameter Tuning and Modeling of a Rotary Kiln using Physics-Informed Neural Networks
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis
PathoLM: Identifying Pathogenicity From The DNA Sequence Through The Genome Foundation Model
Physics-Informed Neural Networks for Derivative-Constrained PDEs
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
PIED: Physics-Informed Experimental Design For Inverse Problems
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
Population Transformer: Learning Population-level Representations of Intracranial Activity
Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference
Predicting dark matter halo masses from simulated galaxy images and environments
Processing large-scale Graphs with G-Signatures
Projection Killer: peering through high dimensional posterior distribution
Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
Quantum circuit synthesis with diffusion models
RamanSPy: Augmenting Raman Spectroscopy Data Analysis with AI
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
Retrieve to Explain: Evidence-driven Predictions with Language Models
RNA-FrameFlow for de novo 3D RNA Backbone Design
RNAInvBench: Benchmark for the RNA Inverse Design Problem
Robust Learning of Transfer Functions for Single-Cell Transcriptomics Depth Normalization
Scalable Anomaly Detection in Batch Polishing Processes for Inertial Confinement Fusion Shells
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
Scalable unsupervised alignment of metric and nonmetric structures
ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks
Scaling Automated Quantum Error Correction Discovery with Reinforcement Learning
Scaling Up Diffusion and Flow-based XGBoost Models
SE(3)-Equivariant Diffusion Graph Nets: Synthesizing Flow Fields by Denoising Invariant Latents on Graphs
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion
Self-supervised learning for crystal property prediction via denoising
SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Smoke and Mirrors in Causal Downstream Tasks
Sorting Out Quantum Monte Carlo
Spectrum-Informed Multistage Neural Network: Multiscale Function Approximator of Machine Precision
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
Symbolic Regression with a Learned Concept Library
Synthetic Data-driven Prediction of Height for Childhood Malnutrition
Tail Extrapolation in target-aware conditional molecule generation
TarDis: Achieving Robust and Structured Disentanglement of Multiple Covariates
Task Addition in Multi-Task Learning by Geometrical Alignment
Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning
The Convolution-Closed Hurdle Motif With an Application to Tensor Decomposition
The Efficacy of Pre-training in Chemical Graph Out-of-distribution Generalization
The Scaling Law in Astronomical Time Series Data
Topological Neural Networks go Persistent, Equivariant and Continuous
Towards detailed and interpretable hybrid modeling of continental-scale bird migration
Towards Enforcing Hard Physics Constraints in Operator Learning Frameworks
Towards Reliable Uncertainty Estimates for Drug Discovery: A Large-scale Temporal Study of Probability Calibration
Training Compute-Optimal Protein Language Models
Training-free Design of Augmentations with Data-centric Principles
Transfer Learning in Multi-fidelity Surrogate Modeling: A Wind Farm Case
TriageAgent: Towards Better Multi-Agents Collaborations for Large Language Model-Based Clinical Triage
Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models
Unfolding Time: Generative Modeling for Turbulent Flows in 4D
Unmixing Noise from Hawkes Process to Model Learned Physiological Events
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
Variable Star Light Curves in Koopman Space
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses