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