Publications

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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

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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

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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

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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

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Preprint

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

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

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In preparation

Development of bespoke interatomic potential for plastic strength simulation via information-matching active learning

Yonatan Kurniawan, Logan Williams, Ilia Nikiforov, Amit Samanta, Mark K. Transtrum, Vincenzo Lordi, Vasily V. Bulatov

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