Search Results for author: Armi Tiihonen

Found 4 papers, 2 papers with code

Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

1 code implementation23 May 2021 Qiaohao Liang, Aldair E. Gongora, Zekun Ren, Armi Tiihonen, Zhe Liu, Shijing Sun, James R. Deneault, Daniil Bash, Flore Mekki-Berrada, Saif A. Khan, Kedar Hippalgaonkar, Benji Maruyama, Keith A. Brown, John Fisher III, Tonio Buonassisi

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 high-throughput experimentation systems.

Active Learning Benchmarking +2

Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

2 code implementations16 Mar 2023 Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, Tonio Buonassisi

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-processed semiconductors.

Band Gap

Learning relevant contextual variables within Bayesian Optimization

no code implementations23 May 2023 Julien Martinelli, Ayush Bharti, Armi Tiihonen, S. T. John, Louis Filstroff, Sabina J. Sloman, Patrick Rinke, Samuel Kaski

Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.

Bayesian Optimization Model Selection

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