211 citations · 969 across the 37 of their papers we have counts for
5 papers · 1 filter
Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design
Matthew R. Carbone, Hyeong Jin Kim, Chandima Fernando +7
The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in v…
Accurate, uncertainty-aware classification of molecular chemical motifs from multi-modal X-ray absorption spectroscopy
Matthew R. Carbone, Phillip M. Maffettone, Xiaohui Qu +2
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry and biology. In this work, we present neu…
Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles
Animesh Ghose, Mikhail Segal, Fanchen Meng +7
As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important…
Machine-Learning X-ray Absorption Spectra to Quantitative Accuracy
Matthew R. Carbone, Mehmet Topsakal, Deyu Lu +1
The advent of massive data repositories has propelled machine learning techniques to the front lines of many scientific fields, and exploring new frontiers by leveraging the predic…
Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning
Matthew R. Carbone, Shinjae Yoo, Mehmet Topsakal +1
X-ray absorption spectroscopy is a premier element-specific technique for materials characterization. Specifically, the x-ray absorption near-edge structure (XANES) encodes importa…