4 citations · 6 across the 15 of their papers we have counts for
17 papers
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?
Dongyeop Woo, Nofit Segal, Nayoung Kim +3
Determining crystal structures from powder X-ray diffraction (PXRD) would enable routine characterization of newly synthesized materials, including those absent from reference data…
Packora: Systematic Design for Generative Molecular Crystal Structure Prediction
Nayoung Kim, Kiyoung Seong, Sungsoo Ahn
Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing…
MADField: Multi-fidelity Amortized Density Field for Adsorption in Nanoporous Materials
Yoonho Kim, Seongsu Kim, Sungsoo Ahn +1
High-throughput computational screening of nanoporous materials for gas storage and separation requires fast and accurate characterization of adsorption equilibrium. Particle-based…
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…
AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction
Nayoung Kim, Honghui Kim, Sihyun Yu +3
Metal-organic frameworks (MOFs) are promising materials for applications such as direct air capture, where performance depends on how adsorbates bind within the framework. Accurate…
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…
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…
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…
Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
Minsu Kim, Jean-Pierre Falet, Oliver E. Richardson +5
Chain-of-Thought (CoT) reasoning has advanced the capabilities and transparency of language models (LMs); however, reasoning chains can contain inaccurate statements that reduce pe…
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Nayoung Kim, Seongsu Kim, Minsu Kim +2
Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce…
Generative Flows on Synthetic Pathway for Drug Design
Seonghwan Seo, Minsu Kim, Tony Shen +4
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synt…
Adaptive teachers for amortized samplers
Minsu Kim, Sanghyeok Choi, Taeyoung Yun +7
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is in…
Pessimistic Backward Policy for GFlowNets
Hyosoon Jang, Yunhui Jang, Minsu Kim +2
This paper studies Generative Flow Networks (GFlowNets), which learn to sample objects proportionally to a given reward function through the trajectory of state transitions. In thi…
Decoupled Sequence and Structure Generation for Realistic Antibody Design
Nayoung Kim, Minsu Kim, Sungsoo Ahn +1
Recently, deep learning has made rapid progress in antibody design, which plays a key role in the advancement of therapeutics. A dominant paradigm is to train a model to jointly ge…
Gaussian Plane-Wave Neural Operator for Electron Density Estimation
Seongsu Kim, Sungsoo Ahn
This work studies machine learning for electron density prediction, which is fundamental for understanding chemical systems and density functional theory (DFT) simulations. To this…
Learning Energy Decompositions for Partial Inference of GFlowNets
Hyosoon Jang, Minsu Kim, Sungsoo Ahn
This paper studies generative flow networks (GFlowNets) to sample objects from the Boltzmann energy distribution via a sequence of actions. In particular, we focus on improving GFl…
Local Search GFlowNets
Minsu Kim, Taeyoung Yun, Emmanuel Bengio +4
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ab…