Catalysis
informatics is a distinct subfield that lies at the intersection
of cheminformatics and materials informatics but with distinctive
challenges arising from the dynamic, surface-sensitive, and multiscale
nature of heterogeneous catalysis. The ideas behind catalysis informatics
can be traced back decades, but the field is only recently emerging
due to advances in data infrastructure, statistics, machine learning,
and computational methods. In this work, we review the field from
early works on expert systems and knowledge engines to more recent
approaches utilizing machine-learning and uncertainty quantification.
The data–information–knowledge hierarchy is introduced
and used to classify various developments. The chemical master equation
and microkinetic models are proposed as a quantitative representation
of catalysis knowledge, which can be used to generate explanative
and predictive hypotheses for the understanding and discovery of catalytic
materials. We discuss future prospects for the field, including improved
quantitative coupling of experiment/theory, advanced microkinetic
models, and the development of open-source software tools. Ultimately,
integration of existing chemical and physical models with emerging
statistical and computational tools presents a promising route toward
the automated design, discovery, and optimization of heterogeneous
catalytic processes.
In this study, calibration maintenance confronts the problem of updating a model developed in the primary condition to accurately predict the calibrated analyte in samples measured in new secondary conditions. Calibration transfer refers to updating a model based on a primary instrument to predict samples measured on new secondary instruments. A 2-norm variant of Tikhonov regularization (TR) has been used with spectroscopic data to perform calibration maintenance and transfer where just a few samples measured in the secondary condition/instrument are augmented to the primary calibration data to update the primary model. To achieve improved predictions, in this paper we report on 1-norm penalties to create two novel variants of TR for model updating. To solve the multiple penalty minimization numerical problems involved with the new TR variants, data transformation processes are applied, allowing a least absolute shrinkage and selection operator type algorithm to be implemented. Near-infrared spectra measured under two different temperatures represent the calibration maintenance application, and near-infrared spectra measured on two instruments denote the calibration transfer situation. Compared to TR in the recently developed 2-norm penalty mode, validation sample prediction errors are reduced when the 1-norm TR variant that selects wavelengths is used.
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