Active oversight and quality control in standard Bayesian optimization for autonomous experiments
Abstract The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a sec...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01485-2 |
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| author | Sumner B. Harris Rama Vasudevan Yongtao Liu |
| author_facet | Sumner B. Harris Rama Vasudevan Yongtao Liu |
| author_sort | Sumner B. Harris |
| collection | DOAJ |
| description | Abstract The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization. |
| format | Article |
| id | doaj-art-81a251d7beeb4194bc7df6860dfc29ee |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-81a251d7beeb4194bc7df6860dfc29ee2025-02-02T12:33:50ZengNature Portfolionpj Computational Materials2057-39602025-01-011111910.1038/s41524-024-01485-2Active oversight and quality control in standard Bayesian optimization for autonomous experimentsSumner B. Harris0Rama Vasudevan1Yongtao Liu2Center for Nanophase Materials Sciences, Oak Ridge National LaboratoryCenter for Nanophase Materials Sciences, Oak Ridge National LaboratoryCenter for Nanophase Materials Sciences, Oak Ridge National LaboratoryAbstract The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.https://doi.org/10.1038/s41524-024-01485-2 |
| spellingShingle | Sumner B. Harris Rama Vasudevan Yongtao Liu Active oversight and quality control in standard Bayesian optimization for autonomous experiments npj Computational Materials |
| title | Active oversight and quality control in standard Bayesian optimization for autonomous experiments |
| title_full | Active oversight and quality control in standard Bayesian optimization for autonomous experiments |
| title_fullStr | Active oversight and quality control in standard Bayesian optimization for autonomous experiments |
| title_full_unstemmed | Active oversight and quality control in standard Bayesian optimization for autonomous experiments |
| title_short | Active oversight and quality control in standard Bayesian optimization for autonomous experiments |
| title_sort | active oversight and quality control in standard bayesian optimization for autonomous experiments |
| url | https://doi.org/10.1038/s41524-024-01485-2 |
| work_keys_str_mv | AT sumnerbharris activeoversightandqualitycontrolinstandardbayesianoptimizationforautonomousexperiments AT ramavasudevan activeoversightandqualitycontrolinstandardbayesianoptimizationforautonomousexperiments AT yongtaoliu activeoversightandqualitycontrolinstandardbayesianoptimizationforautonomousexperiments |