Multi-fidelity cost-aware Bayesian optimization

“…Z. Foumani et al proposed a cost-aware MF acquisition function that performs exploration and exploitation using low and high fidelity data, respectively. 58 This approach was utilised to design nanolaminate ternary alloys and hybrid organic-inorganic perovskite crystals. As another example, C. Fare et al proposed Targeted Variance Reduction (TVR) which uses multi-fidelity data to compute the optimal acquisition function taking into account the evaluation cost and accuracy.…”

Section: Multi-fidelity Problemmentioning

confidence: 99%

Bayesian optimisation for efficient material discovery: a mini review

Kumar

Jin

2023

Nanoscale

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“…Currently, existing strategies include EIpu, 36 EI-cool, 43 ACBO (Adaptive cost-aware Bayesian optimization), 42 and so on. [45][46][47][48][49] Originally, these strategies are constructed based on the EI acquisition function. As research progresses, there appear other non-greedy costaware approaches, such as the lookahead method, 48 despite a substantial increase in computational cost.…”

Section: Inclusion Of Experimental Cost Functionsmentioning

confidence: 99%

“…Currently, existing strategies include EIpu, 36 EI‐cool, 43 ACBO (Adaptive cost‐aware Bayesian optimization), 42 and so on 45–49 . Originally, these strategies are constructed based on the EI acquisition function.…”

Section: Cost‐aware Parallelized Bayesian Optimization (Capbo)mentioning

confidence: 99%

CAPBO: A cost‐aware parallelized Bayesian optimization method for chemical reaction optimization

Liang,

Hu,

Han

et al. 2023

AIChE Journal

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Bayesian optimization employs probabilistic surrogate models to effectively address expensive and time‐consuming closed‐loop chemical experimental design. However, traditional Bayesian optimization focuses on reducing the number of iterations and follows an inherently sequential process (with one new data point sampled in each iteration), which is an inefficient means of exploiting and characterizing reactions using parallel microreactors. In this article, we present an approach that overcomes this issue by considering two essential factors: experimental cost sensitivity and parallelization. We propose a novel cost‐aware parallelized Bayesian optimization method (CAPBO) in which the goal is changed from reducing the number of iterations to reducing the experimental cost, with quantitative experimental speed‐up achieved through parallelization. The combination of these two items greatly fits the need of reaction optimization, leading to better optimization effects. Benchmarking and case studies demonstrate that the proposed method significantly outperforms traditional Bayesian optimization and considerably improves the optimization efficiency while maintaining the simplicity of the framework.

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Multi-fidelity cost-aware Bayesian optimization

Cited by 31 publications

References 59 publications

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Bayesian optimisation for efficient material discovery: a mini review

CAPBO: A cost‐aware parallelized Bayesian optimization method for chemical reaction optimization

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