19 citations · 39 across the 10 of their papers we have counts for
4 papers · 1 filter
Charting the thermodynamic stability of hybrid perovskite alloys with machine learning
Jarno Laakso, Armi Tiihonen, Patrick Rinke
Alloy-based perovskite semiconductor materials offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development of solar c…
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…