24 citations · 28 across the 9 of their papers we have counts for
9 papers
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…
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…
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes proper…
AutoREC: A reinforcement learning platform for equivalent circuit model generation
Ali Jaberi, Yonatan Kurniawan, Robert Black +5
This paper introduces AutoREC, an open-source Python platform for developing, training and evaluating reinforcement learning (RL) agents that automatically generate equivalent circ…
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…
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…
Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling
Yonatan Kurniawan, Cody L. Petrie, Mark K. Transtrum +4
Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model's predictions…
Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
Yonatan Kurniawan, Cody L. Petrie, Kinamo J. Williams +5
In this paper, we consider the problem of quantifying parametric uncertainty in classical empirical interatomic potentials (IPs) using both Bayesian (Markov Chain Monte Carlo) and…
Embracing Uncertainty in "Small Data" Problems: Estimating Earthquakes from Historical Anecdotes
Nathan E. Glatt-Holtz, Ronald A. Harris, Andrew J. Holbrook +4
Seismic risk estimates will be vastly improved with an increased understanding of historical (and pre-historical) seismic events. However the only existing data for these events is…