ICLR 2025 Past Other
AI for Accelerated Materials Design - ICLR 2025
AI4MAT-ICLR-2025
- Submission deadline
- Feb 4, 2025, 12:00 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 (66)
Fetched from OpenReview (v2) on 2026-06-10.
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3D Microstructure Reconstruction of Aerogels via Conditional GANs
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A Foundation Model for Simulation-Grade Molecular Electron Densities
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A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
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Accelerated Gradient-Based Design Optimization via Differentiable Physics Informed Neural Operator for Composite Materials Processing
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Accelerated Photocatalytic C–C Coupling via Interpretable Deep Learning: Single-Crystal Perovskite Catalyst Design using First-Principles Calculations
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Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
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Active and transfer learning with partially Bayesian neural networks for materials and chemicals
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All-atom Diffusion Transformers: Unified generative modelling of molecules and materials
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AQForge: Bridging Generative Models and Property Prediction for Materials Discovery
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Automated Data Extraction from Solar Cell Literature Using Large Language Models
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Benchmarking Band Gap Prediction for Semiconductor Materials using Multimodal and Multi-Fidelity Data
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Benchmarking Text Representations for Crystal Structure Generation with Large Language Models
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Capturing Global Features of Crystals from Their Bond Networks
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Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
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CrysLDM: Latent Diffusion Model for Crystal Material Generation
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Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers
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CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning
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Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes
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DEQuify your force field: More efficient simulations using deep equilibrium models
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Detecting Symmetry-Breaking in Molecular Data Distributions
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DIRECT PREDICTION OF TENSORIAL PROPERTIES WITH EQUIVARIANT MESSAGE-PASSING: APPLICATIONS TO NONLINEAR OPTICS
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Dis-CSP: Disordered crystal structure predictions
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Does this smell the same? Learning representations of olfactory mixtures using inductive biases
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Dynamic Fusion for a Multimodal Foundation Model for Materials
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ELECTRA: A Symmetry-breaking Cartesian Network for Charge Density Prediction with Floating Orbitals
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Evaluating Machine Learning Potentials on Bulk Structures with Neutral Substitutional Defects
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Evaluating Universal Interatomic Potentials for Molecular Dynamics of Real-World Minerals
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Feature Informed Batch Selection may Accelerate Training and Tuning of Chemical Foundation Models
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Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations
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In-Context Fine-Tuning for Neural Operators
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It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design
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Kinetic Langevin Diffusion for Crystalline Materials Generation
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Large Language Models Are Innate Crystal Structure Generators
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LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases
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Lifting the benchmark iceberg with item-response theory
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LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval
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LLM-as-Judge Meets LLM-as-Optimizer: Enhancing Organic Data Extraction Evaluations Through Dual LLM Approaches
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LLM-Augmented Chemical Synthesis and Design Decision Programs
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MatAgent: A human-in-the-loop multi-agent LLM framework for accelerating the material science discovery cycle
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MatBind: Probing the multimodality of materials science with contrastive learning
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MatDock: Multi-molecule docking in porous materials with flow matching
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MatFusion: A Multi-Modal Framework Bridging LLMs and Structural Embeddings for Experimental Materials Property Prediction
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MatInvent: Reinforcement Learning for 3D Crystal Diffusion Generation
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MATMMFUSE: MULTI-MODAL FUSION MODEL FOR MATERIAL PROPERTY PREDICTION
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MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data
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MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform
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MoMa: A Modular Deep Learning Framework for Material Property Prediction
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nanoMINER: Multimodal Information Extraction for Nanomaterials
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NeuralDEM: Real-time Simulation of Industrial Particulate Flows
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Open Materials Generation with Stochastic Interpolants
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OPERATING ROBOTIC LABORATORIES WITH LARGE LANGUAGE MODELS AND TEACHABLE AGENTS
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PLaID: Preference Aligned Language Model for Targeted Inorganic Materials Design
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PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
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Reliability of Deep Learning Models for Scanning Electron Microscopy Analysis
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Representing surfactants by foundation models
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Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
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Revealing chemical reasoning in LLMs through search on complex planning tasks
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Semantic Device Graphs for Perovskite Solar Cell Design
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SMI-TED: A large-scale foundation model for materials and chemistry
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Tango*: Constrained synthesis planning using chemically informed value functions
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TDCM25: A Multi-Modal Multi-Task Benchmark for Temperature-Dependent Crystalline Materials
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Towards Extrapolation in Deep Material Property Regression
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Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians
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Towards Faster and More Compact Foundation Models for Molecular Property Prediction
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Transformer as a Neural Knowledge Graph
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What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization