1 citations · 1 across the 3 of their papers we have counts for
6 papers
Machine Learning Hamiltonians are Accurate Energy-Force Predictors
Seongsu Kim, Chanhui Lee, Yoonho Kim +7
Recently, machine learning Hamiltonian (MLH) models have gained traction as fast approximations of electronic structures such as orbitals and electron densities, while also enablin…
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
Hanbum Ko, Chanhui Lee, Ye Rin Kim +4
Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (LLMs) have recently shown promising res…
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling
Kiyoung Seong, Sungsoo Ahn, Sehui Han +1
Crystal modeling spans a family of conditional and unconditional generation tasks, including crystal structure prediction (CSP) and de novo generation (DNG). While recent deep gene…
MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Hojung Jung, Rodrigo Hormazabal, Jaehyeong Jo +5
Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely ad…
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
Jaewan Lee, Changyoung Park, Hongjun Yang +3
Recent advancements in graph neural networks (GNNs) have significantly enhanced the prediction of material properties by modeling crystal structures as graphs. However, GNNs often…
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Chanhui Lee, Hanbum Ko, Yuheon Song +6
Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Larg…