ICLR 2026 Past Other
AI for Accelerated Materials Design - ICLR 2026
AI4Mat-ICLR-2026
- Submission deadline
- Feb 2, 2026, 20:00 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (56)
Fetched from OpenReview (v2) on 2026-06-10.
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A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes
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Accelerating Multi-Property Molecular Design via Entropic-Risk-Based Counterfactual Explanations
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AI-Guided Closed-Loop Discovery of Hard Multiple Principal Element Alloys
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An Experiment-Aware Bayesian Optimization Workflow for Noisy Mixed-Input Settings
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An Orbital-based Geometric Deep Learning Framework for Periodic Materials
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ASTRA: Statistically Robust Model Selection from Cross-Validation
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Benchmarking Augmentation Strategies for LLM-Based Solid-State Synthesis Prediction
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Boltzmann Generators for Condensed Matter via Riemannian Flow Matching
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CatAgent: Multi-Agent Orchestration for Electrocatalyst Discovery
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Challenges and Vision For Standardization of Biopolymer Datasets for Machine Learning
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Characterizing Microelectronic Devices via Scalable, Confinement-Aware Equivariant Networks
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Comparative Performance of EI-MS Spectrum Prediction Models under Data-scarce and Domain-imbalanced Settings
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Context Determines Optimal Architecture in Materials Segmentation
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Discovering Out-of-Distribution Superconductors via Reinforcement Learning and Model Merging
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Diversity-Aware Pretraining in Materials Learning via Task Similarity
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Enforcing Constraints in Molecular and Crystalline Generative Models via Physics-Constrained Flow Matching
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Exploring Transfer Learning for Materials Property Prediction
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Feedback-Based Learning of Ground State Properties using Tensor Cross Interpolation
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FragmentFlow: Scalable Transition State Generation for Large Molecules
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Framework-Constrained Materials Generation
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From Synthesis to Kinetics: A Data-Driven Deep Learning Framework for Process-Aware Ferroelectric Dynamics
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Generative Adversarial Networks for Data Augmentation and Inverse Design of Synthesis Conditions in Perovskite Solar Cells
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Geometry-Aware OOD Generalization for Composite Materials
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Getting the Data Right: A Physics-Consistent, Calibrated Dataset for SEM-Based Defect Localization in PEM Fuel Cells
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Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink
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Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation
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Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning
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Latent Diffusion Pretraining for Crystal Property Prediction
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Learning 4D Material-Interface Dynamics From Few X-RAY Projections
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Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics
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Learning k-Resolved Electronic Structure via Soft Energy Occupancy Prediction
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Materials Research Agent
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MATRIX: Stress-Testing LLM Reasoning in Materials Science
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MatSeek: An Automated Knowledge-Driven Framework for Materials Research
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Molecule property prediction with molecular orbitals
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MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning
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NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra
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Open Challenges to Unlock Deep Eutectic Solvent Discovery
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Open Materials Generation with Inference-Time Reinforcement Learning
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Optimizing Materials With CliqueFlowmer
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Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
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Property-Guided Molecular Generation and Optimization via Latent Flows
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Reasoning-to-Simulation: An Agentic Framework for Discovery of Electrolyte Materials
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Robotic Automation Discovery of Biodegradable Electronics via Multimodal Active Learning and AI-Guided Design
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Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
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Solvaformer: Unified Geometric Learning for Solubility-Aware Automated Synthesis
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Synergistic Multi-Task Learning for Electronic Density of States Prediction
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SynReason: Enhancing Synthesis Reasoning via Reinforcement Learning Experimental Feedback
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Synthesis-constrained molecular design with direct optimization of reaction conditions
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Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra
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Text-Twin-Translation (T$^{3}$): A Full-Stack Machine Learning Framework for Functional Material-Device Systems Discovery
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The Benchmarking Void: A Roadmap for Domain-Adapted Computer Vision in Fuel Cell Defect Detection
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Topology-Aware Neural Graph Operator (TANGO) for Material Constitutive Laws
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Towards Intelligent Manufacturing: Spatio-Temporal Learning of Process–Material Dynamics with Attention-Driven Neural Operators
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Train Separately, Compose at Sampling: Multi-Property Crystal Generation with Orthogonal Flow Guidance
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When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction