Publications

Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials

Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams, Jr., Mark K. Transtrum, Ellad B. Tadmor, Ryan S. Elliott, Daniel S. Karls, Mingjian Wen

The Journal of Chemical Physics, 2022 · DOI

Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling

Yonatan Kurniawan, Cody L. Petrie, Mark K. Transtrum, Ellad B. Tadmor, Ryan S. Elliott, Daniel S. Karls, Mingjian Wen

IEEE 18th International Conference on e-Science, 2022 · DOI

Embracing uncertainty in “small data” problems: estimating earthquakes from historical anecdotes

Nathan E. Glatt-Holtz, Ronald A. Harris, Andrew J. Holbrook, Justin A. Krometis, Yonatan Kurniawan, Hayden Ringer, Jared P. Whitehead

JGR Machine Learning and Computation, 2025 · DOI

An information-matching approach to optimal experimental design and active learning

Yonatan Kurniawan, Tracianne B. Neilsen, Benjamin L. Francis, Alex M. Stankovic, Mingjian Wen, Ilia Nikiforov, Ellad B. Tadmor, Vasily V. Bulatov, Vincenzo Lordi, Mark K. Transtrum

Applied Physics Letters, 2026 · DOI

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials

Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor, Mark K. Transtrum

arXiv

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data

Ali Jaberi, Yonatan Kurniawan, Robert Black, Shayan Mousavi M., Kabir Verma, Zoya Sadighi, Santiago Miret, Jason Hattrick-Simpers

arXiv

Building informative materials datasets beyond targeted objectives

Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang, Yonatan Kurniawan, Hao Wan, Runze Zhang, Adji Bousso Dieng, Kangming Li, Jason Hattrick-Simpers

arXiv

Inverse design of bespoke interatomic potentials via active learning by information‑matching

Yonatan Kurniawan, Logan D. Williams, Amit Samanta, Ilia Nikiforov, Daniel Schwalbe-Koda, Mark K. Transtrum, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov

arXiv

Machine Learning‑Informed Optimization of EIS Measurements for Reducing Low‑Frequency Sampling and Faster Data Acquisition

Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan, Runze Zhang, Ali Jaberi, Ashley Dale, Jason Hattrick-Simpers