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Myung Chul (Michael) Kim
PhD Candidate @ Stanford ME
Monte Carlo & Molecular Dynamics

I am a fourth-year Ph.D. candidate in the Micro and Nano Mechanics Group at Stanford University. I am privileged to be advised by Prof. Wei Cai.

I develop theory and algorithms for importance sampling to (1) accelerate rare events and (2) estimate their dynamic properties. The method biases Langevin dynamics with a graph neural network committor and reweights paths to keep rates unbiased despite committor error.

It matches brute-force rates within 3% for bcc Fe vacancy migration, resolves 12 transition modes of Pt(001) adatom diffusion, and extends to alanine dipeptide in vacuum.

With collaborators, I model strain in Si–Ge–Sn nanowires and dislocation mobility in fcc Cu, and develop Burgers-vector identification for dark-field X-ray microscopy.


Education

  • Stanford University

    Ph.D. in Mechanical Engineering

    2023 - Present

    Advisor: Prof. Wei Cai

  • Korea Advanced Institute of Science and Technology (KAIST)

    B.S. in Mechanical Engineering

    B.S. in Electrical Engineering

    2019 - 2023

Awards and Honors

  • James D. Plummer Graduate Fellowship

    Stanford University (2023 - 2024)

  • National Science & Technology Scholarship

    Korea Ministry of Science and ICT (2019 - 2023)

  • Global Leadership Award

    KAIST (2023)

  • President's Undergraduate Research Award

    Georgia Institute of Technology (2022)

  • KSME | SEMES Open Innovation Challenge

    The Korean Society of Mechanical Engineers (KSME) | SEMES (2022)

  • Young Engineers Honors Society

    National Academy of Engineering of Korea (2020)

  • Guwon Academic Excellence Scholarship

    Guwon Scholarship Foundation (2020)

  • KAIST Academic Excellence Scholarship

    KAIST (2020)

Service and Teaching Activities

  • Summer Undergraduate Research Fellow Mentor

    Machine Learning Interatomic Potentials for Large Scale Molecular Dynamics (Summer 2025)

  • Teaching Assistant

    ME 123: Computational Engineering (Spring 2026)

    ME 346A: Introduction to Statistical Mechanics (Winter 2025)

  • Undergraduates Mentored

    Saul Eduardo Perez Herrera (Summer 2025) → National Autonomous University of Mexico

    Eitan Cohen Arazi (Winter 2024) → University of Buenos Aires


Publications

  • Accelerated Langevin Dynamics Simulation via Neural Network–Driven Importance Sampling

    Michael Kim, Wei Cai

    ChemRxiv Preprint (2026) Link PDF

  • Accelerated Markov Chain Monte Carlo Simulation via Neural Network–Driven Importance Sampling

    Michael Kim, Wei Cai

    ArXiv Preprint (2026) Link PDF

  • Smart Filtering Facepiece Respirator with Self-Adaptive Fit and Wireless Humidity Monitoring

    Kangkyu Kwon, Yoon Jae Lee, Yeongju Jung, Ira Soltis, Yewon Na, Lissette Romero, Myung Chul Kim, Nathan Rodeheaver, Hodam Kim, Chaewon Lee, Seung-Hwan Ko, Jinwoo Lee, Woon-Hong Yeo

    Biomaterials (2025) Link

  • Stretchable Wearable Wireless Bioelectronics Using All Printed Pressure Sensors and Strain Gauges

    Nathan Zavanelli, Yoon Jae Lee, Myung Chul Kim, Allison Bateman, Matthew Guess, Hyeonseok Kim, Dinesh K Patel, Woon–Hong Yeo

    Advanced Materials Technologies (2024) Link Journal Cover

  • Advances in Electrochemical Sensors for Detecting Analytes in Biofluids

    Jimin Lee, Myung Chul Kim, Ira Soltis, Sung Hoon Lee, Woon-Hong Yeo

    Advanced Sensor Research (2023) Link Journal Cover

Conferences

Includes upcoming events

  • Accelerated Langevin Dynamics Simulation via Neural Network-Driven Importance Sampling

    ChemAI NYC 2026, Simons Center for Computational Physical Chemistry, New York University

    New York, NY (June 2026)

  • Accelerating Langevin Dynamics Simulation of Rare Events

    ICME Research Symposium 2026, Stanford Institute for Computational & Mathematical Engineering

    Stanford, CA (May 2026)

  • Accelerating Rare Event Simulations

    AI+Science: Accelerating Discovery, Stanford HAI

    Stanford University (May 2026)

  • Neural Network Driven Importance Sampling for Accelerated Kinetic Monte Carlo of Rare Events

    MRS Spring Meeting, Materials Research Society

    Honolulu, HI (Apr. 2026)

  • Accelerating Monte Carlo Simulation of Rare Events by Importance Sampling using Neural Network

    DAMOP 2025, The 56th Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics

    Portland, OR (June 2025) Link

  • Smart Filtering Facepiece Respirator with Adaptive Fit and Wireless Humidity Monitoring

    BMES 2022 Annual Meeting, Biomedical Engineering Society

    San Antonio, TX (Oct. 2022)

  • Stretchable Pressure Sensor using Inter-Digitated Serpentine Structure

    Gordon Research Conference, Multifunctional Materials and Structures

    Ventura, CA (Sept. 2022) Link

Patents

  • A method and apparatus for classification of subtypes of cells with morphological and motility features using hybrid learning

    Hyunjong Shin, Chanhong Min, Minwoo Kang, Hyuntae Jeong, Taeyoon Kwon, Myung Chul Kim

    KR Patent 10-2762542 (2025) Link


Research

Energy landscape partitioned into two metastable basins, with success and failure transition paths

Accelerated Langevin Dynamics

A neural-network importance function biases Langevin dynamics toward rare transitions, and path weights absorb its errors: rates and channel splits match Kramers theory on a two-channel potential and brute force for LJ7, and a revision in preparation extends the method to a Pt adatom and a vacancy in iron.

Two-dimensional energy landscape on a grid, with minima A and B, saddle points S1 and S2, and a lattice path from A to B

Accelerated MCMC

A neural network learns a bias potential in log space that steers lattice Monte Carlo through rare transitions, while path reweighting and a branching random walk keep the success-probability estimate unbiased, closely matching exact rates on 2-D and 14-D model landscapes and Kramers theory from 500 to 1000 K.

Three panels: accelerated LJ7 transition paths in collective-variable space, sampled Pt adatom paths on the Pt(001) surface, and the bcc iron supercell around a vacancy

Accelerated Atomistic Simulations

Neural-network importance sampling of Langevin dynamics reproduces reference rates and resolves competing channels, including an unanticipated long jump, for the LJ7 cluster and, in a revised manuscript in preparation, a Pt adatom on Pt(001) and a vacancy in bcc iron.

Alanine dipeptide in the beta/C5 metastable conformation

Agent-Guided Variance Reduction for Protein Kinetics

An agentic search driven by Claude Code selects the weight-control scheme that tames path-weight degeneracy, recovering the alanine dipeptide isomerization rate to within 3–6% of brute-force MD.


Notes

  • Reinforcement Learning [Poster]

    Stanford, Winter 2026

  • Mathematical Finance [PDF]

    Stanford, Winter 2026

  • Computational Methods of Applied Mathematics [PDF]

    Stanford, Winter 2026

  • Advanced Physical Chemistry [PDF]

    Stanford, Winter 2025

  • Finite Element Analysis [PDF]

    Stanford, Winter 2025

  • Introduction to Statistical Mechanics

    Problem Session Notes [PDF]

    Stanford, Winter 2025

  • Introduction to Non-Equilibrium Statistical Mechanics [PDF]

    Thermal Activation of Dislocation Glide [PDF]

    Stanford, Fall 2024

  • Numerical Linear Algebra [PDF]

    Stanford, Fall 2024

  • Defects and Disorder in Materials [PDF]

    Stanford, Spring 2024

  • Elasticity and Inelasticity [PDF]

    Stanford, Spring 2024

  • Stochastic Differential Equations [PDF]

    Stanford, Winter 2024

  • Partial Differential Equations [PDF]

    Stanford, Winter 2024

  • Linear Algebra [PDF]

    Stanford, Fall 2023

  • Applied Quantum Mechanics [PDF]

    Stanford, Fall 2023

  • Waves in Solids and Fluids [PDF]

    KAIST, Fall 2021

  • Mechanism Design [PDF]

    KAIST, Fall 2021