Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data
| Autor: | Toshiaki Taniike, Thanh Nhat Nguyen, Lauren Takahashi, Ashutosh Thakur, Keisuke Takahashi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010405 organic chemistry
business.industry Computer science 010402 general chemistry 01 natural sciences 0104 chemical sciences Catalysis General Materials Science Oxidative coupling of methane Physical and Theoretical Chemistry Process engineering business Throughput (business) Selection (genetic algorithm) |
| Zdroj: | The Journal of Physical Chemistry Letters. 11:6819-6826 |
| ISSN: | 1948-7185 |
| DOI: | 10.1021/acs.jpclett.0c01926 |
| Popis: | Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data. Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C |
| Databáze: | OpenAIRE |
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