3 papers
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…
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…
cs.CL2024
Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification
Zeren Shui, Petros Karypis, Daniel S. Karls +4
Citation intention Classification (CIC) tools classify citations by their intention (e.g., background, motivation) and assist readers in evaluating the contribution of scientific l…