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