Accepted Work

All accepted work 🌟 denotes spotlight Work

• NeuralDEM: Real-time Simulation of Industrial Particulate Flows

• In-Context Fine-Tuning for Neural Operators

• TDCM25: A Multi-Modal Multi-Task Benchmark for Temperature-Dependent Crystalline Materials

• Reliability of Deep Learning Models for Scanning Electron Microscopy Analysis

• It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design

• Accelerated Gradient-Based Design Optimization via Differentiable Physics Informed Neural Operator for Composite Materials Processing

• 🌟 All-atom Diffusion Transformers

• 🌟 Towards Extrapolation in Deep Material Property Regression

• Feature Informed Batch Selection may Accelerate Training and Tuning of Chemical Foundation Models

• Representing surfactants by foundation models

• A Foundation Model for Simulation-Grade Molecular Electron Densities

• SMI-TED: A large-scale foundation model for materials and chemistry

• Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning

• CrysLDM: Latent Diffusion Model for Crystal Material Generation

• ELECTRA: A Symmetry-breaking Cartesian Network for Charge Density Prediction with Floating Orbitals

• MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data

• Accelerated Photocatalytic C–C Coupling via  Interpretable Deep Learning: Single-Crystal Perovskite Catalyst Design using First-Principles Calculations

• Kinetic Langevin Diffusion for Crystalline Materials Generation

• 🌟 MoMa: A Modular Deep Learning Framework for Material Property Prediction

• 🌟 Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

• Lifting the benchmark iceberg with item-response theory

• 🌟 MatBind: Probing the multimodality of materials science with contrastive learning

• Semantic Device Graphs for Perovskite Solar Cell Design

• Dis-CSP: Disordered crystal structure predictions

• Automated Data Extraction from Solar Cell Literature Using Large Language Models

• LLM-as-Judge Meets LLM-as-Optimizer: Enhancing Organic Data Extraction Evaluations Through Dual LLM Approaches

• A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories

• Benchmarking Text Representations for Crystal Structure Generation with Large Language Models

• Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations

• AQForge: Bridging Generative Models and Property Prediction for Materials Discovery

• Tango*: Constrained synthesis planning using chemically informed value functions

• Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning

• 🌟 DEQuify your force field: More efficient simulations using deep equilibrium models

• DIRECT PREDICTION OF TENSORIAL PROPERTIES WITH EQUIVARIANT MESSAGE-PASSING: APPLICATIONS TO NONLINEAR OPTICS

• Capturing Global Features of Crystals from Their Bond Networks

• MatDock: Multi-molecule docking in porous materials with flow matching

• 3D Microstructure Reconstruction of Aerogels via Conditional GANs

• 🌟 Compositional Flows for 3D Molecule and Synthesis Pathway Co-design

• Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers

• Evaluating Machine Learning Potentials on Bulk Structures with Neutral Substitutional Defects

• 🌟 OPERATING ROBOTIC LABORATORIES WITH LARGE LANGUAGE MODELS AND TEACHABLE AGENTS

• Active and transfer learning with partially Bayesian neural networks for materials and chemicals

• Benchmarking Band Gap Prediction for Semiconductor Materials using Multimodal and Multi-Fidelity Data

• Transformer as a Neural Knowledge Graph

• MatAgent: A human-in-the-loop multi-agent LLM framework for accelerating the material science discovery cycle

• 🌟 CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learning

• Detecting Symmetry-Breaking in Molecular Data Distributions

• 🌟 MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials through an Open and Accessible Benchmark Platform

• 🌟 Open Materials Generation with Stochastic Interpolants

• 🌟 PLaID: Preference Aligned Language Model for Targeted Inorganic Materials Design

• Data Curation for Machine Learning Interatomic Potentials by Determinantal Point Processes

• LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval

• Large Language Models Are Innate Crystal Structure Generators

• MatFusion: A Multi-Modal Framework Bridging LLMs and Structural Embeddings for Experimental Materials Property Prediction

• LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases

• Towards Faster and More Compact Foundation Models for Molecular Property Prediction

• nanoMINER: Multimodal Information Extraction for Nanomaterials

• 🌟 Evaluating Universal Interatomic Potentials for Molecular Dynamics of Real-World Minerals

• Dynamic Fusion for a Multimodal Foundation Model for Materials

• Revealing chemical reasoning in LLMs through search on complex planning tasks

• PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration

• What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization

• MatInvent: Reinforcement Learning for 3D Crystal Diffusion Generation

• LLM-Augmented Chemical Synthesis and Design Decision Programs

• 🌟 Does this smell the same? Learning representations of olfactory mixtures using inductive biases

• MATMMFUSE: MULTI-MODAL FUSION MODEL FOR MATERIAL PROPERTY PREDICTION