Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Gundry, Luke; Guo, SiXuan; Kennedy, Gareth; Keith, Jonathan M.; Robinson, Michael F.; Gavaghan, David; Bond, Alan M.

doi:10.1039/d0cc07549c

Cited by 59 publications

(50 citation statements)

References 116 publications

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“…However, methods and protocols for reliably solving the inverse problem in a statistical framework still need significant investigation. [53,66] This work confirms that Bayesian inference approaches are powerful, but need to be implemented with caution. Indeed their automated introduction without careful scrutiny of the fidelity of parameters recovered is fraught with danger.…”

Section: Discussionsupporting

confidence: 68%

“…Once an approximate value has been calculated using the HarmPer objective function, the parameters can be identified by the more complex MLE‐TCDS or MLE‐ExpHarmPer objective functions. As such, the output parameter values from MLE‐TCDS or MLE‐ExpHarmPer functions can be assessed with a decree of confidence that the parameters fit are reasonable and chemically intelligent when compared to the HarmPer objective function output [53] …”

Section: Resultsmentioning

confidence: 99%

“…As such, the output parameter values from MLE-TCDS or MLE-ExpHarmPer functions can be assessed with a decree of confidence that the parameters fit are reasonable and chemically intelligent when compared to the HarmPer objective function output. [53]…”

Section: Comparison Of Data Analysis Methods Using Synthetic Datamentioning

confidence: 99%

“…This form of dynamic intelligence is not available in mathematical forms of optimisation or Bayesian inference as implemented in this paper, although biasing the outcome in a preferable direction via smart application of the correct log-likelihood is possible [42] and in the future machine learning could be introduced for this purpose. [53,66] In practice, the heuristic method only provides a single point parameter estimate, does not report the system reproducibility and is so tedious that only about three parameters can be varied and compared in a series of 5 in rows 3 and 6 respectively. Application of MLE-TCDS with Inference method could not be reliably fit for D and R u so other parameters were then fitted using experimental or literature values as described in the text.…”

Section: Row Bayesian Inference Methodsmentioning

confidence: 99%

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A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

et al. 2021

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This paper is dedicated to the memory of Jean-Michel SavéantUse of carefully designed computer supported parameterisation methods in voltammetric studies can provide highly robust and accurate methods for simultaneously quantifying the large number of parameters present in complex electrochemical reactions. In this study, a computer program has been developed to parameterise large amplitude AC voltammetric data using mathematical optimisation in combination with Bayesian inference algorithms for calculating posterior distributions of parameters and hence uncertainties in parameter values. The computer program has been applied to objective functions, relevant to total AC current, frequency domain data in the form of the power spectrum derived from Fourier transformation and multivariate based methods using the resolved harmonic data. The robustness of the objective functions have been confirmed and Bayesian inference methods have been validated using "noisy" synthetic and experimental data for the [Fe(CN) 6 ] 3À /4À reduction process in aqueous 3.0 M KCl electrolyte at a gold electrode. It was found that the harmonic based Bayesian inference methods outperformed other methods in parameterisation of the thermodynamics and electrode kinetics of the close to reversible [Fe(CN) 6 ] 3À /4À process due to their ability to compensate for non-ideality in the modelling and the superior parameter sensitivities available in the higher harmonics. The computer supported and heuristic methods were compared. Their advantages and limitations are discussed.

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Section: Discussionsupporting

confidence: 68%

Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Data Analysis Methods Using Synthetic Datamentioning

confidence: 99%

Section: Row Bayesian Inference Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

et al. 2021

Self Cite

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“…Full details of the mathematical model, as well as a more in-depth description of the optimization methods used to solve the inverse problem are provided in the Supplemantary Material ( Gavaghan et al, 2018 ; Clerx et al, 2019 ; Harris et al, 2020 ; Gundry et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

A Voltammetric Perspective of Multi-Electron and Proton Transfer in Protein Redox Chemistry: Insights From Computational Analysis of Escherichia coli HypD Fourier Transformed Alternating Current Voltammetry

et al. 2021

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This paper explores the impact of pH on the mechanism of reversible disulfide bond (CysS-SCys) reductive breaking and oxidative formation in Escherichia coli hydrogenase maturation factor HypD, a protein which forms a highly stable adsorbed film on a graphite electrode. To achieve this, low frequency (8.96 Hz) Fourier transformed alternating current voltammetric (FTACV) experimental data was used in combination with modelling approaches based on Butler-Volmer theory with a dual polynomial capacitance model, utilizing an automated two-step fitting process conducted within a Bayesian framework. We previously showed that at pH 6.0 the protein data is best modelled by a redox reaction of two separate, stepwise one-electron, one-proton transfers with slightly “crossed” apparent reduction potentials that incorporate electron and proton transfer terms (Eapp20 > Eapp10). Remarkably, rather than collapsing to a concerted two-electron redox reaction at more extreme pH, the same two-stepwise one-electron transfer model with Eapp20 > Eapp10 continues to provide the best fit to FTACV data measured across a proton concentration range from pH 4.0 to pH 9.0. A similar, small level of crossover in reversible potentials is also displayed in overall two-electron transitions in other proteins and enzymes, and this provides access to a small but finite amount of the one electron reduced intermediate state.

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Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)

Chen,

Yang,

Smetana

et al. 2024

Angewandte Chemie

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Machine learning is increasingly integrated into chemistry research by guiding experimental procedures, correlating structure, and function, interpreting large experimental datasets, to distill scientific insights that might be challenging with traditional methods. Such applications, however, largely focus on gaining insights via big data and/or big computation, while neglecting the valuable chemical prior knowledge dwelling in chemists’ minds. In this paper, we introduce an Electrochemistry‐Informed Neural Network (ECINN) by explicitly embedding electrochemistry priors including the Butler‐Volmer (BV), Nernst and diffusion equations on the backbone of neural networks for multi‐task discovery of electrochemistry parameters. We applied the ECINN to voltammetry experiments of and redox couples to discover electrode kinetics and mass transport parameters. Notably, ECINN seamlessly integrated mass transport with BV to analyze the entire voltammogram to infer transfer coefficients directly, so offering a new approach to Tafel analysis by outdating various mass transport correction methods. In addition, ECINN can help discover the nature of electron transfer and is shown to refute incorrect physics if imposed. This work encourages chemists to embed their domain knowledge into machine learning models to start a new paradigm of chemistry‐informed machine learning for better accountability, interpretability, and generalization.

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Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

Abstract: Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry.

Cited by 59 publications

References 116 publications

A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

A Voltammetric Perspective of Multi-Electron and Proton Transfer in Protein Redox Chemistry: Insights From Computational Analysis of Escherichia coli HypD Fourier Transformed Alternating Current Voltammetry

Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)

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