Accepted Work

All accepted work 🌟 denotes Spotlight 

• Optimizing Materials With CliqueFlowmer

• Test-Time Tuned Language Models Enable End-to-end De Novo Molecular Structure Generation from MS/MS Spectra

• Learning 4D Material-Interface Dynamics From Few X-RAY Projections

• Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink

• From Synthesis to Kinetics: A Data-Driven Deep Learning Framework for Process-Aware Ferroelectric Dynamics

• The Benchmarking Void: A Roadmap for Domain-Adapted Computer Vision in Fuel Cell Defect Detection

• Train Separately, Compose at Sampling: Multi-Property Crystal Generation with Orthogonal Flow Guidance

• An Experiment-Aware Bayesian Optimization Workflow for Noisy Mixed-Input Settings

• Molecule property prediction with molecular orbitals

• 🌟 Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics

• MatSeek: An Automated Knowledge-Driven Framework for Materials Research

• Sample Efficient Generative Molecular Optimization with Joint Self-Improvement

• Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach

• SynReason: Enhancing Synthesis Reasoning via Reinforcement Learning Experimental Feedback

• Challenges and Vision For Standardization of Biopolymer Datasets for Machine Learning

• 🌟 Open Materials Generation with Inference-Time Reinforcement Learning

• Property-Guided Molecular Generation and Optimization via Latent Flows

• Solvaformer: Unified Geometric Learning for Solubility-Aware Automated Synthesis

• 🌟 NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra

• Getting the Data Right: A Physics-Consistent, Calibrated Dataset for SEM-Based Defect Localization in PEM Fuel Cells

• ASTRA: Statistically Robust Model Selection from Cross-Validation

• Exploring Transfer Learning for Materials Property Prediction

• Context Determines Optimal Architecture in Materials Segmentation

• MSP-LLM: A Unified Large Language Model Framework for Complete Material Synthesis Planning

• Synergistic Multi-Task Learning for Electronic Density of States Prediction

• Latent Diffusion Pretraining for Crystal Property Prediction

• Reasoning-to-Simulation: An Agentic Framework for Discovery of Electrolyte Materials

• Learning k-Resolved Electronic Structure via Soft Energy Occupancy Prediction

• 🌟 Text-Twin-Translation (T**3): A Full-Stack Machine Learning Framework for Functional Material-Device Systems Discovery

• AI-Guided Closed-Loop Discovery of Hard Multiple Principal Element Alloys

• FragmentFlow: Scalable Transition State Generation for Large Molecules

• 🌟 Synthesis-constrained molecular design with direct optimization of reaction conditions

• MATRIX: Stress-Testing LLM Reasoning in Materials Science

• When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction

• Feedback-Based Learning of Ground State Properties using Tensor Cross Interpolation

• Towards Intelligent Manufacturing: Spatio-Temporal Learning of Process–Material Dynamics with Attention-Driven Neural Operators

• An Orbital-based Geometric Deep Learning Framework for Periodic Materials

• Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

• Topology-Aware Neural Graph Operator (TANGO) for Material Constitutive Laws

• A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes

• Benchmarking Augmentation Strategies for LLM-Based Solid-State Synthesis Prediction

• CatAgent: Multi-Agent Orchestration for Electrocatalyst Discovery

• Information-Theoretic Requirements for Gradient-Based Task Affinity Estimation in Multi-Task Learning

• Accelerating Multi-Property Molecular Design via Entropic-Risk-Based Counterfactual Explanations

• Framework-Constrained Materials Generation

• Comparative Performance of EI-MS Spectrum Prediction Models under Data-scarce and Domain-imbalanced Settings

• 🌟 Characterizing Microelectronic Devices via Scalable, Confinement-Aware Equivariant Networks

• 🌟 Diversity-Aware Pretraining in Materials Learning via Task Similarity

• 🌟 Discovering Out-of-Distribution Superconductors via Reinforcement Learning and Model Merging

• Materials Research Agent

• 🌟 Robotic Automation Discovery of Biodegradable Electronics via Multimodal Active Learning and AI-Guided Design

• Geometry-Aware OOD Generalization for Composite Materials

• 🌟 Boltzmann Generators for Condensed Matter via Riemannian Flow Matching

• Generative Adversarial Networks for Data Augmentation and Inverse Design of Synthesis Conditions in Perovskite Solar Cells

• Enforcing Constraints in Molecular and Crystalline Generative Models via Physics-Constrained Flow Matching

• Open Challenges to Unlock Deep Eutectic Solvent Discovery