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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.00544 |
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| _version_ | 1866908388451942400 |
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| author | Sabanza-Gil, Víctor Barbano, Riccardo Gutiérrez, Daniel Pacheco Luterbacher, Jeremy S. Hernández-Lobato, José Miguel Schwaller, Philippe Roch, Loïc |
| author_facet | Sabanza-Gil, Víctor Barbano, Riccardo Gutiérrez, Daniel Pacheco Luterbacher, Jeremy S. Hernández-Lobato, José Miguel Schwaller, Philippe Roch, Loïc |
| contents | Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research Sabanza-Gil, Víctor Barbano, Riccardo Gutiérrez, Daniel Pacheco Luterbacher, Jeremy S. Hernández-Lobato, José Miguel Schwaller, Philippe Roch, Loïc Machine Learning Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences. |
| title | Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.00544 |