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Arxiv
3d
171
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Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects
- Bayesian Optimization (BO) is increasingly used in materials science for experimental optimization tasks.
- A study was conducted to simulate batch BO with six design variables and different noise levels.
- Two test cases, Ackley function and Hartmann function, relevant for materials science problems were examined.
- The study analyzed the impact of noise, batch-picking method, acquisition function, and hyperparameter values on optimization outcomes.
- Noise was found to have varying effects depending on the problem landscape.
- Noise degraded optimization results more in a needle-in-a-haystack search scenario, but increased the probability of finding a local optimum in the Hartmann function.
- Prior knowledge of the problem domain structure and noise level is crucial when designing BO for materials research experiments.
- Synthetic data studies help evaluate the impact of different batch BO components before moving to real experimental systems.
- The study results aim to enhance the utilization of BO in guiding experimental materials research with a large number of design variables.
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