collaborators

7 papers

physics.data-an2026

Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency Data

Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan +4

Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by th…

cond-mat.mtrl-sci2026

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

Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor +1

Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful t…

cond-mat.mtrl-sci2026

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

Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6

Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…

cond-mat.mtrl-sci2026

Building informative materials datasets beyond targeted objectives

Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6

Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers p…

cs.LG2026

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 +5

This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circuit models (ECMs) from elect…

cs.LG2026

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

Yonatan Kurniawan, Tracianne B. Neilsen, Benjamin L. Francis +7

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applicatio…