11 citations · 13 across the 3 of their papers we have counts for
4 papers
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
Rishi E. Kumar, Armi Tiihonen, Shijing Sun +3
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse. In this perspective, we review practical challenges hi…
Discovering Equations that Govern Experimental Materials Stability under Environmental Stress using Scientific Machine Learning
Richa Ramesh Naik, Armi Tiihonen, Janak Thapa +4
While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, inductive reasoning and knowledge extraction remain elusive tasks, in part…
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
Qiaohao Liang, Aldair E. Gongora, Zekun Ren +12
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and hig…
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Zekun Ren, Shijing Sun +9
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-en…