SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints

Adachi, Masaki; Hayakawa, Satoshi; Hamid, Saad; Jørgensen, Martin Bak; Oberhauser, Harald; Osborne, Micheal A.

doi:10.48550/arxiv.2301.11832

Cited by 2 publications

(2 citation statements)

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“…Related challenges arise in batch selection for Bayesian optimisation, where the key challenge is determining a diverse set of samples for the task of maximising an expensive 'black-box' function. [112][113][114][115][116] Two of the critical design choices in ensemble learning are, which classi-ers to combine, and how to combine them. Common approaches include bagging and boosting methods, which are well described in ref.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)

Niroomand,

Dicks,

Pyzer-Knapp

et al. 2024

Digital Discovery

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Section: Ensemble Methodsmentioning

confidence: 99%

Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)

Niroomand,

Dicks,

Pyzer-Knapp

et al. 2024

Digital Discovery

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show abstract

“…16 Furthermore, machine learning methods gained popularity during the last years for analyzing spectral data such as FTIR spectra, 17,18 Raman spectra, [18][19][20] X-ray diffraction spectra, 21 and EIS data. 14,[22][23][24][25][26][27] Recent developments include Bayesian model selection for EIS data 28,29 based on fast Bayesian inference using quadrature. 30,31 Further development of software tools and ML methods for analyzing impedance data can be accelerated by the publication of open-source software libraries and data sets.…”

mentioning

confidence: 99%

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

Schaeffer,

Gasper,

Garcia-Tamayo

et al. 2023

J. Electrochem. Soc.

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Analysis of electrochemical impedance spectroscopy (EIS) data for electrochemical systems often consists of defining an equivalent circuit model (ECM) using expert knowledge and then optimizing the model parameters to deconvolute various resistance, capacitive, inductive, or diffusion responses. For small data sets, this procedure can be conducted manually; however, it is not feasible to manually define a proper ECM for extensive data sets with a wide range of EIS responses. Automatic identification of an ECM would substantially accelerate the analysis of large sets of EIS data. We showcase machine learning methods to classify the ECMs of 9300 impedance spectra provided by QuantumScape for the BatteryDEV hackathon. The best-performing approach is a gradient-boosted tree model utilizing a library to automatically generate features, followed by a random forest model using the raw spectral data. A convolutional neural network using boolean images of Nyquist representations is presented as an alternative, although it achieves a lower accuracy. We publish the data and open source the associated code. The approaches described in this article can serve as benchmarks for further studies. A key remaining challenge is the identifiability of the labels, underlined by the model performances and the comparison of misclassified spectra.

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SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints

Cited by 2 publications

References 0 publications

Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)

Insights into machine learning models from chemical physics: an energy landscapes approach (EL for ML)

Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra

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