Accepted Work

All accepted work 🌟 denotes spotlight Work

• Graph Representation of Local Environments for Learning High-Entropy Alloy Properties

• A Mamba-Based Foundation Model for Chemistry

• 🌟 A Chemically-Guided Generative Diffusion Model for Materials Synthesis Planning

• Generating ideal synthetic data for 3D reconstruction of FIB tomography data using generative adversarial networks

• 🌟 HoneyComb: A Flexible LLM-Based Agent System for Materials Science

• Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

• 🌟 Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

• Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models

• A Large Encoder-Decoder Polymer-Based Foundation Model

• 🌟 Constrained Synthesis with Projected Diffusion Models

• Adaptive Representation of MOFs in Bayesian Optimization

• XRayPro: A self-supervised multimodal model for MOF application recommendations using PXRD and precursors

• MOTIFNet: Automating the Analysis of Amphiphile and Block Polymer Self-Assembly from SAXS Data

• LatentDE: Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design

• Multi-View Mixture-of-Experts for Predicting Molecular Properties Using SMILES, SELFIES, and Graph-Based Representations

• Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation

• Deterministic global optimization for sample-efficient molecular design with generative machine learning

• Avoiding Post-Processing with Context: Texture Boundary Detection in Metallography

• Benchmarking of Universal Machine Learning Interatomic Potentials for Structural Relaxation

• Accelerating Quantum Emitter Characterization with Latent Neural Ordinary Differential Equations

• Contrastive Language–Structure Pre-training Driven by Materials Science Literature

• Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules?

• Leveraging Large Language Models for Explaining Material Synthesis Mechanisms: The Foundation of Materials Discovery

• Sim2Real transfer for catalyst activity prediction

• Multi-modal cascade feature transfer for polymer property prediction

• Efficient Autoencoder Pipeline for Discovering High Entropy Alloys with Molecular Dynamics Data

• SELF-BART : A Transformer-based Molecular Representation Model using SELFIES

• Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations

• Microstructure modeling of deformed alloys using contrastive conditional generative adversarial networks

• Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes

• Diversity-Based Two-Phase Pruning Strategy for Maximizing Image Segmentation Generalization with applications in Transmission Electron Microscopy

• SAFE setup for generative molecular design

• Dynamic Beam Enumeration: A Bridge Between Generative Molecular Design and Library Screening

• RHAAPsody: RHEED Heuristic Adaptive Automation Platform Framework for Molecular Beam Epitaxy Synthesis

• Leveraging Pre-Trained LMs for Rapid and Accurate Structure Elucidation from 2D NMR Data

• Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search

• Evaluating Chemistry Prompts for Large-Language Model Fine-Tuning

• Optimal Spectroscopic Measurement Design: Bayesian Framework for Rational Data Acquisition

• Reaction Graph Networks for Inorganic Synthesis Condition Prediction of Solid State Materials

• Discovering Multi-Layer Films for Electromagnetic Interference Shielding and Passive Cooling with Multi-Objective Active Learning

• Simultaneous Discovery of Reaction Coordinates and Committor Functions Using Equivariant Graph Neural Networks

• 🌟 ChemLit-QA: A human evaluated dataset for chemistry RAG tasks

• 🌟 MaCBench: A multimodal chemistry and materials science benchmark

• Chemical Language Meets Geometric Graphs: A Multimodal Fusion Approach for Molecular Properties

• Force Field Optimization by End-to-End Differentiable Atomistic Simulation

• Automated Atomic Force Microscopy Using Large Language Models

• 🌟 Deconstructing equivariant representations in molecular systems

• Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literature

• A Geometric Foundation Model for Crystalline Material Discovery

• ML Force Fields for Computational NMR Spectra of Dynamic Materials across Time-Scales

• A Physics Enforced Neural Network to Predict Polymer Melt Viscosity

• MatExpert: Decomposing Materials Discovery By Mimicking Human Experts

• LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction

• Perovs-Dopants: Machine Learning Potentials for Doped Bulk Structures

• Human-in-the-loop interface for Automated experiments in Electron Microscopy, Automated characterization

• Force-Controlled Robotic Mechanochemical Synthesis

• 🌟 Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules

• Towards Autonomous Nanomaterials Synthesis via Reaction-Diffusion Coupling

• Learning to Optimize Molecules with a Chemical Language Model

• 🌟 3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models

• 🌟 SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

• 🌟 Advancing the ColabFit Exchange towards a Web-scale Data Source for Machine Learning Interatomic Potentials

• 🌟 dZiner: Rational Inverse Design of Materials with AI Agents

• Autonomous robotic experimentation system for powder X-ray diffraction

• Scaling autoregressive models for lattice thermodynamics

• Automatic solid form classification in pharmaceutical drug development

• Spectro: A multi-modal approach for molecule elucidation using IR and NMR data

• 🌟 Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces

• MolGen-Transformer: An open-source self-supervised model for Molecular Generation and Latent Space Exploration

• WyckoffTransformer: Generation of Symmetric Crystals

• Large scale Extraction of Composition and Properties from Materials Tables

• 🌟 Scientific Knowledge Graph and Ontology Generation using Open Large Language Models

• Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach

• Efficient Design-and-Control Automation with Reinforcement Learning and Adaptive Exploration

• If optimizing for general parameters in chemistry is useful, why is it hardly done?

• LLaMat: Large Language Models for Materials Science Information Extraction

• Epitaxial Thin Film Interface Imaging with Deep Learning

• Equivariant conditional diffusion model for exploring the chemical space around Vaska’s complex