19 citations · 39 across the 10 of their papers we have counts for
10 papers
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…
Multi-objective Bayesian Optimization with Human-in-the-Loop for Flexible Neuromorphic Electronics Fabrication
Benius Dunn, Javier Meza-Arroyo, Armi Tiihonen +2
Neuromorphic computing hardware enables edge computing and can be implemented in flexible electronics for novel applications. Metal oxide materials are promising candidates for fab…
Virtual Laboratories: Domain-agnostic workflows for research
Carlos Sevilla-Salcedo, Armi Tiihonen, Mahsa Asadi +4
Many scientific disciplines have traditionally advanced by iterating over hypotheses using labor-intensive trial-and-error, which is a slow and expensive process. Recent advances i…
Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
Imon Mia, Armi Tiihonen, Anna Ernst +4
Bayesian Optimization (BO) machine learning method is increasingly used to guide experimental optimization tasks in materials science. To emulate the large number of input variable…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…
Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization
Alexander E. Siemenn, Eunice Aissi, Fang Sheng +4
High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-p…
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…
Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
Armi Tiihonen, Sarah J. Cox-Vazquez, Qiaohao Liang +12
New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug, takes decades. Although progress…