Accepted Work

All accepted work 🌟 denotes spotlight Work

• Pretrained Graph Transformers for Chirality-related Tasks

• 🌟 Explainable Deep Multi-View Methodology for HEDP Foam Quality Assessment

• Using machine-learning and large-language-model extracted data to predict copolymerizations

• 🌟 MatText: Do Language Models Need More than Text & Scale for Materials Modeling?

• 🌟 Transfer learning based on atomic feature extraction for the prediction of experimental C13 chemical shifts

• 🌟 An Advanced Physics-Informed Neural Operator for Dynamic, Zero-shot, and Near Real-time Simulation of Aerospace Composite Material Curing Process

• 🌟 MMSci: A Multimodal Multi-Discipline Dataset for PhD-Level Scientific Comprehension

• Efficient Evolutionary Search over Chemical Space with Large Language Models

• 🌟 Bridging chemical modalities by aligning embeddings

• Scaling Computational Performance of Spherical Harmonics Kernels with Triton

• KUSP: Python server for deploying ML interatomic potentials

• 🌟 Low-Dimensional Projections for Visualizing Energy Landscapes of Atomic Systems

• A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation

• LLaMat: Large Language Models for Materials Science

• Extraction of Compositions from Texts of Material Science Articles

• Are LLMs Ready for Real-World Materials Discovery?

• 🌟 Knowledge Graph Extraction from Total Synthesis Documents