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