NeurIPS 2024 Past Other
AI for Accelerated Materials Design - NeurIPS 2024
AI4Mat-NeurIPS-2024
- Submission deadline
- Sep 7, 2024, 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 (78)
Fetched from OpenReview (v2) on 2026-06-10.
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3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models
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A Chemically-Guided Generative Diffusion Model for Materials Synthesis Planning
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A Geometric Foundation Model for Crystalline Material Discovery
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A Large Encoder-Decoder Polymer-Based Foundation Model
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A Mamba-Based Foundation Model for Chemistry
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A Physics Enforced Neural Network to Predict Polymer Melt Viscosity
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Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations
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Adaptive Representation of MOFs in Bayesian Optimization
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Advancing the ColabFit Exchange towards a Web-scale Data Source for Machine Learning Interatomic Potentials
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Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations
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Automated Atomic Force Microscopy Using Large Language Models
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Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literature
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Automatic solid form classification in pharmaceutical drug development
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Autonomous robotic experimentation system for powder X-ray diffraction
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Avoiding Post-Processing with Context: Texture Boundary Detection in Metallography
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Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes
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Benchmarking of Universal Machine Learning Interatomic Potentials for Structural Relaxation
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Chemical Language Meets Geometric Graphs: A Multimodal Fusion Approach for Molecular Properties
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ChemLit-QA: A human evaluated dataset for chemistry RAG tasks
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Constrained Synthesis with Projected Diffusion Models
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Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
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Contrastive Language–Structure Pre-training Driven by Materials Science Literature
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Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach
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Deconstructing equivariant representations in molecular systems
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Deterministic global optimization for sample-efficient molecular design with generative machine learning
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Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules?
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Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
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Discovering Multi-Layer Films for Electromagnetic Interference Shielding and Passive Cooling with Multi-Objective Active Learning
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Diversity-Based Two-Phase Pruning Strategy for Maximizing Image Segmentation Generalization with applications in Transmission Electron Microscopy
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Dynamic Beam Enumeration: A Bridge Between Generative Molecular Design and Library Screening
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dZiner: Rational Inverse Design of Materials with AI Agents
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Efficient Autoencoder Pipeline for Discovering High Entropy Alloys with Molecular Dynamics Data
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Efficient Design-and-Control Automation with Reinforcement Learning and Adaptive Exploration
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Epitaxial Thin Film Interface Imaging with Deep Learning
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Equivariant conditional diffusion model for exploring the chemical space around Vaska’s complex
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Evaluating Chemistry Prompts for Large-Language Model Fine-Tuning
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Force Field Optimization by End-to-End Differentiable Atomistic Simulation
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Force-Controlled Robotic Mechanochemical Synthesis
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Generating ideal synthetic data for 3D reconstruction of FIB tomography data using generative adversarial networks
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Graph Representation of Local Environments for Learning High-Entropy Alloy Properties
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HoneyComb: A Flexible LLM-Based Agent System for Materials Science
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Human-in-the-loop interface for Automated experiments in Electron Microscopy, Automated characterization
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If optimizing for general parameters in chemistry is useful, why is it hardly done?
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Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
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Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules
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Large scale Extraction of Composition and Properties from Materials Tables
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LatentDE: Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design
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Learning to Optimize Molecules with a Chemical Language Model
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Leveraging Large Language Models for Explaining Material Synthesis Mechanisms: The Foundation of Materials Discovery
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Leveraging Pre-Trained LMs for Rapid and Accurate Structure Elucidation from 2D NMR Data
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LLaMat: Large Language Models for Materials Science Information Extraction
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LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
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MaCBench: A multimodal chemistry and materials science benchmark
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MatExpert: Decomposing Materials Discovery By Mimicking Human Experts
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Microstructure modeling of deformed alloys using contrastive conditional generative adversarial networks
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ML Force Fields for Computational NMR Spectra of Dynamic Materials across Time-Scales
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MolGen-Transformer: An open-source self-supervised model for Molecular Generation and Latent Space Exploration
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MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data
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Multi-modal cascade feature transfer for polymer property prediction
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Multi-View Mixture-of-Experts for Predicting Molecular Properties Using SMILES, SELFIES, and Graph-Based Representations
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Optimal Spectroscopic Measurement Design: Bayesian Framework for Rational Data Acquisition
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Perovs-Dopants: Machine Learning Potentials for Doped Bulk Structures
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Reaction Graph Networks for Inorganic Synthesis Condition Prediction of Solid State Materials
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RHAAPsody: RHEED Heuristic Adaptive Automation Platform Framework for Molecular Beam Epitaxy Synthesis
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SAFE setup for generative molecular design
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Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
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Scaling autoregressive models for lattice thermodynamics
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Scientific Knowledge Graph and Ontology Generation using Open Large Language Models
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SELF-BART : A Transformer-based Molecular Representation Model using SELFIES
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Sim2Real transfer for catalyst activity prediction
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Simultaneous Discovery of Reaction Coordinates and Committor Functions Using Equivariant Graph Neural Networks
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Spectro: A multi-modal approach for molecule elucidation using IR and NMR data
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SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
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Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
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Towards Autonomous Nanomaterials Synthesis via Reaction-Diffusion Coupling
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Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation
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WyckoffTransformer: Generation of Symmetric Crystals
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XRayPro: A self-supervised multimodal model for MOF application recommendations using PXRD and precursors