A Primer about Machine Learning in Catalysis – A Tutorial with Code

Palkovits, Stefan

doi:10.1002/cctc.202000234

Cited by 31 publications

(34 citation statements)

References 18 publications

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“…We would be remiss not to briefly discuss the emerging field of informatics for catalysis, which is growing rapidly because of its potential to revolutionize the discovery and design of catalysts. Recently, ChemCatChem published a series of papers as a special collection highlighting the use of data science in catalysis. − Although computational chemists have long used informatics approaches, , the confluence of high-throughput synthesis, characterization, and testing of catalysts, and the proliferation of computational capabilities have driven the development of machine learning and data science tools . These tools are enabling the analysis of both experimental and computed data in an effort to discover hidden relationships and connect chemical properties with catalytic activity .…”

Section: Lessons Learned and Future Opportunitiesmentioning

confidence: 99%

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

et al. 2020

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Sustainable energy generation calls for a shift away from centralized, high-temperature, energy-intensive processes to decentralized, low-temperature conversions that can be powered by electricity produced from renewable sources. Electrocatalytic conversion of biomass-derived feedstocks would allow carbon recycling of distributed, energy-poor resources in the absence of sinks and sources of high-grade heat. Selective, efficient electrocatalysts that operate at low temperatures are needed for electrocatalytic hydrogenation (ECH) to upgrade the feedstocks. For effective generation of energy-dense chemicals and fuels, two design criteria must be met: (i) a high H:C ratio via ECH to allow for high-quality fuels and blends and (ii) a lower O:C ratio in the target molecules via electrochemical decarboxylation/deoxygenation to improve the stability of fuels and chemicals. The goal of this review is to determine whether the following questions have been sufficiently answered in the open literature, and if not, what additional information is required: What organic functionalities are accessible for electrocatalytic hydrogenation under a set of reaction conditions? How do substitutions and functionalities impact the activity and selectivity of ECH? What material properties cause an electrocatalyst to be active for ECH? Can general trends in ECH be formulated based on the type of electrocatalyst? What are the impacts of reaction conditions (electrolyte concentration, pH, operating potential) and reactor types?

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Section: Lessons Learned and Future Opportunitiesmentioning

confidence: 99%

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

et al. 2020

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“…[17][18][19] The effectiveness of machine learning for the development of highly active OCM catalysts, based on a well-edited dataset, has also been emphasized by Suzuki et al [20] and Palkovits. [21] Although machine learning and data mining open up the insight on a powerful strategy for designing catalysts, [17][18][19][20][21] catalyst design on the basis of chemical and structural roles in the OCM reaction must be important from the fundamental point of view. Recently, Matsumoto et al [22] investigated the construction of well-defined active sites focusing on phase-pure crystalline oxides and discovered a quasi-ternary oxide Li 2 CaSiO 4 as a highly active OCM catalyst.…”

Section: Introductionmentioning

confidence: 99%

Effect of B Site Substitution on the Catalytic Activity of La‐Based Perovskite for Oxidative Coupling of Methane

Gan

Nishida

Haneda

2022

Physica Status Solidi (b)

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The effect of B site element in LaBO3 (B = Al, Ga, In, Yb, Co, Mn, Fe) perovskites on the catalytic performance for the oxidative coupling of methane (OCM) reaction is investigated. Among the catalysts tested, LaAlO3, LaGaO3, LaInO3, and LaYbO3 show higher activity for the formation of C2 hydrocarbons in the OCM reaction. From the kinetic study on LaAlO3 and LaCoO3, which are the highest and lowest active catalysts, the CH3• formation, as the first step in the OCM reaction, via the reaction of CH4 with oxygen is strongly affected by the B site element. X‐ray photoelectron spectroscopy analysis and the 16O/18O isotopic exchange reaction suggest that the role of the B site element in LaBO3 perovskite is to create enough amount of surficial mobile oxygen species.

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“…One area where ML is gaining more and more traction are novel energy conversion and storage technologies. These techniques are, in particular, intensely explored for application to the development of technologies typically associated with sustainable generation and use of energy such as advanced types (organic and inorganic materials based) of solar cells and LED (light-emitting diodes) [10][11][12][13][14][15][16][17][18][19][20][21][22], inorganic and organic metal ion batteries [23,24], fuel cells and generally heterogeneous catalysis including electro-and photocatalysis [25][26][27][28][29][30][31][32][33][34]. This is natural in the sense that the development of these technologies often passes through optimization and balancing of multiple factors acting simultaneously and to opposite ends; for example, in the case of organic solar cells, there is an optimum to be sought between the donor's bandgap, the band offset between the donor and the acceptor, the reorganization energies of both the donor and the acceptor, the charge transfer integral etc.…”

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

Ihara¹,

Manzhos²

2022

Preprint

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Machine learning (ML) has found increasing use in research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we will focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with sub-dimensional functions by combining the high-dimensional model representation ansatz with machine learning methods like neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy.

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A Primer about Machine Learning in Catalysis – A Tutorial with Code

Cited by 31 publications

References 18 publications

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

Electrocatalytic Hydrogenation of Biomass-Derived Organics: A Review

Effect of B Site Substitution on the Catalytic Activity of La‐Based Perovskite for Oxidative Coupling of Methane

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

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