NeurIPS 2025 Past Other
AI for Accelerated Materials Design - NeurIPS 2025
AI4Mat-NeurIPS-2025
- Submission deadline
- Aug 24, 2025, 14:55 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 (113)
Fetched from OpenReview (v2) on 2026-06-10.
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3DGrid-LLM: Token-Level Fusion of Language and 3D Grids for Chemical Multimodal Generation
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A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery
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A Computational Workflow for Cost-Effective Synthesis of Inorganic Materials: Integrating Thermodynamics, Cellular Automata, Machine Learning, and Commercial Databases
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A Generative Diffusion Model for Amorphous Materials
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A Physics-Informed Neural Network Approach to the Point Defect Model for Electrochemical Oxide Film Growth
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A Synthesizability-Guided Pipeline for Materials Discovery
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Accelerated Discovery of High-Performance Polyamines for Solid-State Direct CO$_2$ Capture via Efficient Simulations and Bayesian Optimization
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Accelerated Inorganic Materials Design with Generative AI Agents
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Accelerating Material Discovery for Metal Organic Frameworks using Large Language Models
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Accurate Band Gap Prediction in Porous Materials using $\Delta$-Learning
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Adapting General-Purpose Foundation Models for X-ray Ptychography in Low-Data Regimes
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Additive Cook’s Distance Guided Training Set Reduction for Generalizable Foundation Models of Interatomic Potentials
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AI-Guided Design and Discovery of Silicon-Based Anode Materials for Lithium-Ion Batteries
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AMDEN: Amorphous Materials DEnoising Network
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An Effective Machine Learning Frame for Materials Discovery Structured by a Chemical Concept
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An exploration of dataset bias in single-step retrosynthesis prediction
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AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up
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Automated Structure Elucidation at Human-Level Accuracy via a Multimodal Multitask Language Model
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Benchmarking Agentic Systems in Automated Scientific Information Extraction with ChemX
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Benchmarking knowledge transfer methods in de novo materials discovery
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Benchmarking LLMs for atomic-level geometric manipulation in crystals
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Benchmarking Multimodal Large Language Models on Electronic Structure Analysis and Interpretation
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Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
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Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2
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Bridging data-rich and data-poor domains on Lithium-Ion Battery via Scanning Electron Microscopic data through Convolutional Neural Network Transfer Learning
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Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
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Causal-Chemprop: Causal Machine Learning for Molecular Property Prediction and Optimization
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CHEMSETS: How Capable Are Chemistry LLMs?
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CHROMA: Conversational Human-Readable Optical Multilayer Assembly for Natural Language-Driven Inverse Design of Structural Coloration
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CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
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Closed-loop, machine learning–driven optimization of reactor yields in reactive carbon electrolyzers
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Comparative analysis of model-agnostic explanation methods in materials science
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CompGen: A Conditional Generation Framework for Inverse Composition Design of Catalytic Surfaces
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Concept-based Steering of Large Language Models for Conditional Molecular Generation
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Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects
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Continuous Uniqueness and Novelty Metrics for Generative Modeling of Inorganic Crystals
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Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
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Cross Modal Predictive architecture for Material Property prediction
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Data Generation for Benchmarking Deep Learning on Materials Images via Noise Injection and CycleGAN
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Data-driven prediction of polymer surface adhesion using high-throughput MD and hybrid network models
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Differentiable, model-agnostic free energy calculation
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Direct Computation of Viscosity from Differentiable Atomistic Simulations
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Diversity-driven training of machine-learned force fields
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Efficient Nudged Elastic Band Method using Neural Network Bayesian Algorithm Execution
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Efficient Universal Potential Distillation with Pre-trained Students in LightPFP
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Emergent Pose-Invariance in 3D Molecular Representations via Multimodal Learning
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Enabling Accurate and Interpretable Property Prediction with TDiMS in Large Molecules
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Enhancing UV Spectral Prediction through Auxiliary Task, Curriculum Learning, and Curvature Limitation
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Evaluating Diffusion-Based Super-Resolution for Trustworthy Quantitative Metallography
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Factorial Data-Driven Inverse Design of Granular Hydrogels for Targeted Therapeutic Release
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Fine-Tuning Vision-Language Models for Multimodal Polymer Property Prediction
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FORK: First-Order Relational Knowledge Distillation for Machine Learning Interatomic Potentials
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Foundation Models Enabling Multi-Scale Battery Materials Discovery: From Molecules To Devices
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GAP: Guided Diffusion for A Priori Transition State Sampling
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Generalizable Prediction of Mixture Etching Rates Using Graph Neural Networks
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GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
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GO-Diff: Data-free and amortized global structure optimization
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Graph Neural Network Guided Selection of Functional Polymers for Charge Transfer Doping of 2D Materials
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Hierarchical Deep Research with Local–Web RAG: Toward Automated System-Level Materials Discovery
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Integrating Experimental Expertise with Adaptive Bayesian Optimization for Perovskite Synthesis
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Interoperable Natural Language Interfaces for Self-Driving Labs via Model Context Protocol
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Inverse Design of Novel Superconductors via Guided Diffusion
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Language Model Enabled Structure Prediction from Infrared Spectra of Mixtures
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Language Models Enable Data-Augmented Inorganic Materials Synthesis Planning
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LeMat-GenBench: Bridging the gap between crystal generation and materials discovery
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LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature
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LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
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LLM Agents for Knowledge Discovery in Atomic Layer Processing
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Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
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MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature
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MetaGen: A DSL, Database, and Benchmark for VLM-Assisted Metamaterial Generation
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MGB: The Material Generation Benchmark
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Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields
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ML-Driven Discovery of Metastable States
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MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
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Multiscale and Multi-Timestep Switching of Multiple Machine Learning Force Fields for Artificial Intelligence-Driven Materials Simulations
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NaviDiv: A Comprehensive Tool for Monitoring Chemical Diversity in Generative Molecular Design
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Neural Contrast Expansion for Explainable Structure-Property Prediction and Random Microstructure Design
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One Small Step with Fingerprints, One Giant Leap for De Novo Molecule Generation from Mass Spectra
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Pharmacophore-Guided Generative Design of Novel Drug-Like Molecules
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Physics-Constrained Diffusion for Lightweight Composite Material Design
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PolUQBench: A Benchmark Study on Uncertainty Quantification of Polymer Property Prediction
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PolyBind: Effectively Combining Datasets Indexed in Different Representations of Polymers
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PolyCG-Base: A Foundation Model for Universal, State-Aware Coarse-Graining of Linear Polymers
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PolyRecommender: A Multimodal Recommendation System for Polymer Discovery
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Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloys
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Q-CatNet: Leveraging Quantum and Graph Features for Catalyst Simulation and Discovery
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Reciprocal Space Attention for Learning Long-Range Interactions
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SAM-EM: Real-Time Segmentation for Automated Liquid Phase Transmission Electron Microscopy
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Scalable Low-Energy Molecular Conformer Generation with Quantum Mechanical Accuracy
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Scaler Transfer: A Simple and Data-efficient Simulation-to-Real Transfer Scheme for Materials
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Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
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Sim$\rightarrow$Exp-MMNMR: A Benchmark for Simulation-to-Experiment Generalization in Multimodal NMR with Chemistry-Aware Metrics
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Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention
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STR-Bamba: Multimodal Molecular Textual Representation Encoder-Decoder Foundation Model
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Superior Molecular Representations from Intermediate Encoder Layers
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Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion
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Symmetry-Aware Prediction of Electron Localization Functions from Superposed Atomic Densities
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Task Alignment Outweighs Framework Choice in Scientific LLM Agents
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The Loss Landscape of XRD-Based Structure Optimization Is Too Rough for Gradient Descent
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TopoMole: Topological Message Passing Meets Hyperedge Messages
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Towards Dynamic Benchmarks for Autonomous Materials Discovery
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Towards End-to-End Learning of Protein Structure Prediction and Structure-based Sequence Design
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Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
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Training a Foundation Model for Materials on a Budget
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Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
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UFSMatAD: A Unified Framework for Few-Shot Material Anomaly Detection Across Nanofiber SEM and Wafer Imaging
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Universal Machine Learning Interatomic Potentials Enable Accurate Metal–Organic Framework Molecular Modeling
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Universally Converging Representations of Matter Across Scientific Foundation Models
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Unveiling Latent Knowledge in Chemistry Language Models through Sparse Autoencoders
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WallpaperNet: A $p6mm$-Equivariant Graph Neural Network for Molecule Adsorption on Graphene
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When Forces Disagree: A Data-Free Fast Uncertainty Estimate for Direct-Force Pre-trained Neural Network Potentials
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XDIP: A Curated X-ray Absorption Spectrum Dataset for Iron-Containing Proteins