A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs
About
I develop algorithms and software at the intersection of quantum chemistry, high-performance computing, and machine learning—building tools that make first-principles simulation faster, more accurate, and more useful for chemistry.
I am an AI Fellow at KIAS (Korea Institute for Advanced Study). Before joining KIAS in May 2026, I was a postdoctoral researcher at KISTI (Korea Institute of Science and Technology Information).
I received my Ph.D. in Chemistry from KAIST in February 2025, with a dissertation on accelerating quantum chemistry calculations through algorithmic and GPU-level optimization. I completed a dual B.S. in Chemistry and Physics at Hanyang University.
My work appears in journal publications as well as top AI conference proceedings. For a complete academic record, see my CV.
Quantum Chemistry & DFT
Real-space methods, eigensolvers, preconditioning, basis-set design
High-Performance Computing
GPU acceleration, multi-GPU eigenvalue solvers, mixed-precision algorithms
AI for Science
Neural pseudopotentials, generative models for transition states & molecular geometry
Scientific Software
Open-source quantum chemistry packages, scalable numerical methods
Publications
* corresponding author · † co-first / equal contribution. Full list on Google Scholar.
2026
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
2025
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
2024
Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations
Diffusion-Based Generative AI for Exploring Transition States from 2D Molecular Graphs
2023
Gaussian-Approximated Poisson Preconditioner for Iterative Diagonalization in Real-Space Density Functional Theory
Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
2022
System-Specific Separable Basis Based on Tucker Decomposition: Application to Density Functional Calculations
Neural Network-Based Pseudopotential: Development of a Transferable Local Pseudopotential
2020
ACE-Molecule: An Open-Source Real-Space Quantum Chemistry Package
2026
A Mixed-Precision Approach to a Preconditioned Eigensolver for Efficient Density Functional Calculations on AI-Focused GPUs
Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracy
2024
Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations
Diffusion-Based Generative AI for Exploring Transition States from 2D Molecular Graphs
2023
Gaussian-Approximated Poisson Preconditioner for Iterative Diagonalization in Real-Space Density Functional Theory
Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units
2022
System-Specific Separable Basis Based on Tucker Decomposition: Application to Density Functional Calculations
Neural Network-Based Pseudopotential: Development of a Transferable Local Pseudopotential
2020
ACE-Molecule: An Open-Source Real-Space Quantum Chemistry Package
2025
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
2023
GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising