Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data

Kondratenko, Evgenii V.; Schlüter, Michael; Baerns, M.; Linke, David; Holeňa, Martin

doi:10.1039/c4cy01443j

Cited by 89 publications

(80 citation statements)

References 39 publications

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“…Thus, the ML predicted values could not directly lead to novel catalyst designs. Although reports based on ML predictions of experimental results of heterogeneous catalysis are limited in number, some examples are available . For example, catalysts for oxidative coupling of methane (OCM) to C 2 products such as C 2 H 4 and C 2 H 6 have been predicted .…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

et al. 2019

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The literature provides insights for catalyst design and discovery. Effective analysis of reported data using machine learning (ML) methods offers the ability to gain valuable information. However, utilizing the literature in this way has obstacles such as lack of compositional overlaps, bias from prior published data, and low sample counts for many elements. The present study describes an ML approach that considers elemental features as input representations instead of inputting catalyst compositions directly. This ML method has the potential for catalyst discovery, including catalytic reactions with limited catalyst composition overlap in the available data. Oxidative coupling of methane (OCM), water gas shift (WGS), and CO oxidation reactions were chosen to confirm the effectiveness of the proposed method by analysis using several state‐of‐the‐art ML methods. Among the ML methods tested, gradient boosting regression with XGBoost (XGB) provided the best results, and prediction accuracy was improved by the proposed approach for all three reaction types. In addition, a quantitative value of “feature importance score” was calculated to evaluate the most influential input variables on catalyst performance. Finally, catalyst optimization was explored using ML as a “surrogate” model, and the top 20 promising candidate catalysts were identified for the OCM reaction based on the optimization. The advantages of ML in catalysis analysis as well as the difficulties and limitations originating from the complexity of heterogeneous catalysis were explored.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

et al. 2019

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“…The samples are realizations of m = 11 random variables described in (42). Among the considered open formulas is also the open formula Quality 6 fulfilled by the samples belonging to the quality degree 6 (2198 samples).…”

Section: Some Hac-based Quantifiersmentioning

confidence: 99%

“…Four of them are benchmark data sets from the UCI repository of machine learning data [40]: Wine quality, Indian liver patients, Glass identification, and Yeast; the last one originates from an investigation of catalysts for oxidative coupling of methane (OCM) [41], [42].…”

Section: Validation Of the Applicability Of The Quantifiersmentioning

confidence: 99%

“…The abbreviations in the leafs: fia -fixed acidity, cia -citric acid, voa -volatile acidity, resresidual sugar, chl -chlorides, fsd -free sulfur dioxide, tsd -total sulfur dioxide, dns -density, slp -sulphates, alc -alcohol described by continuous variables, those variables were first discretized by even deciles for the maximum weight spanning tree with discrete variables. The vertices of the spanning trees constructed for the wine quality data have been defined in (42). The trees obtained by both considered methods are depicted in Fig.…”

Section: Comparison With Spanning Treesmentioning

confidence: 99%

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Using Copulas in Data Mining Based on the Observational Calculus

Holeňa

Bajer

Scavnicky

2015

IEEE Trans. Knowl. Data Eng.

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The objective of the paper is a contribution to data mining within the framework of the observational calculus, through introducingǵeneralized quantifiers related to copulas. Fitting copulas to multidimensional data is an increasingly important method for analyzing dependencies, and the proposed quantifiers of observational calculus assess the results of estimating the structure of joint distributions of continuous variables by means of hierarchical Archimedean copulas. To this end, the existing theory of hierarchical Archimedean copulas has been slightly extended in the paper: It has been proven that sufficient conditions for the function defining a hierarchical Archimedean copula to be indeed a copula, which have so far been rigorously established only for the special case of fully nested Archimedean copulas, hold in general. These conditions allow to define three new generalized quantifiers, which are then thoroughly validated on four benchmark data sets and one data set from a real-world application The paper concludes by comparing the proposed quantifiers to a more traditional approach -maximum weight spanning trees.

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“…The first uses high-power computing and intricate algorithms, that combine quantum and classical mechanics. [13][14][15] Ultimately, both approaches are needed for finding new catalysts and optimising existing ones. 11,12 The second approach is data-driven, based on modelling catalyst performance using a few simple descriptors.…”

Section: Introductionmentioning

confidence: 99%

Predicting the performance of oxidation catalysts using descriptor models

Madaan

Shiju

Rothenberg

2016

Catal. Sci. Technol.

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Practical solutions in catalysis require catalysts that are active and stable. Mixed metal oxides are robust materials, and as such are often used as industrial catalysts. The problem is that predicting their performance a priori is difficult. Following our work on simple descriptors for supported metals based on Slater-type orbitals, we show here that a similar paradigm holds also for metal oxides. Using the oxidative dehydrogenation of butane to 1,3-butadiene as a model reaction, we synthesised and tested 15 bimetallic mixed oxides supported on alumina. We then built a descriptor model for these oxides, and projected the model's results on a set of 1711 mixed oxide catalysts in silico. Based on the model's predictions, six new bimetallic oxides were then synthesised and tested. Experimental validation showed impressive results, with Q 2 > 0.9, demonstrating the power of these low-cost predictive models. Importantly, no interaction terms were included in the model, showing that even if we think that bimetallic oxide catalysts are highly complex materials, their performance can be predicted using simplistic models. The implications of these findings to catalyst optimisation practices in academia and industry are discussed.Catal. Sci. Technol. This journal is

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Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data

Cited by 89 publications

References 39 publications

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Using Copulas in Data Mining Based on the Observational Calculus

Predicting the performance of oxidation catalysts using descriptor models

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