8 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…
Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist
Kiyoung Seong, Nayoung Kim, Sungsoo Ahn
We introduce Human-AI Co-discovery system (HACO) for scientific algorithm discovery through cross-domain search and sparse human steering. Starting from the goal of generating crys…
CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching
Minkyu Kim, Nayoung Kim, Honghui Kim +1
Discovering heterogeneous catalysts tailored for specific reaction intermediates remains a fundamental bottleneck in materials science. While traditional trial-and-error methods an…
AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction
Nayoung Kim, Honghui Kim, Sihyun Yu +3
Deep generative models have shown promise for modeling metal-organic frameworks (MOFs), but existing approaches (1) rely on coarse-grained representations that assume fixed bond le…
Flexible MOF Generation with Torsion-Aware Flow Matching
Nayoung Kim, Seongsu Kim, Sungsoo Ahn
Designing metal-organic frameworks (MOFs) with novel chemistries is a longstanding challenge due to their large combinatorial space and complex 3D arrangements of the building bloc…
High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction
Seongsu Kim, Nayoung Kim, Dongwoo Kim +1
Density functional theory (DFT) is a fundamental method for simulating quantum chemical properties, but it remains expensive due to the iterative self-consistent field (SCF) proces…