scholarly journals Correction: Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence

2020 ◽  
Vol 56 (81) ◽  
pp. 12256-12256
Author(s):  
Masaru Kondo ◽  
H. D. P. Wathsala ◽  
Makoto Sako ◽  
Yutaro Hanatani ◽  
Kazunori Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reaction Conditions ◽  
Flow Reaction

Correction for ‘Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence’ by Masaru Kondo et al., Chem. Commun., 2020, 56, 1259–1262, DOI: 10.1039/C9CC08526B.

Exploration of flow reaction conditions using machine-learning for enantioselective organocatalyzed Rauhut–Currier and [3+2] annulation sequence

2020 ◽  
Vol 56 (8) ◽  
pp. 1259-1262 ◽  
Author(s):  
Masaru Kondo ◽  
H. D. P. Wathsala ◽  
Makoto Sako ◽  
Yutaro Hanatani ◽  
Kazunori Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow System ◽  
Reaction Conditions ◽  
Flow Reaction

A highly atom-economical enantioselective Rauhut–Currier and [3+2] annulation has been established by flow system and machine-learning-assisted exploration of suitable conditions.

Highly Selective Hydrogenative Conversion to the Tertiary, Secondary, and Primary Amines of Nitriles under Flow Reaction Conditions

ChemSusChem ◽  
2021 ◽  
Author(s):  
Tsuyoshi Yamada ◽  
Kwihwan Park ◽  
Chikara Furugen ◽  
Jing Jiang ◽  
Eisho Shimizu ◽  
...  
Keyword(s):  
Primary Amines ◽  
Reaction Conditions ◽  
Flow Reaction

scholarly journals Reaction Condition Optimization for Non-Oxidative Conversion of Methane Using Artificial Intelligence

2020 ◽  
Author(s):  
Hyun Woo Kim ◽  
Sung Woo Lee ◽  
Gyoung S. Na ◽  
Seung Ju Han ◽  
Seok Ki Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chemical Reaction ◽  
Coke Formation ◽  
Oxidative Conversion ◽  
Reaction Conditions ◽  
Condition Optimization ◽  
Controllable Factors ◽  
Conversion Of Methane ◽  
Oxidative Conversion Of Methane

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C<sub>2</sub> compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH<sub>4</sub> conversion, C<sub>2</sub> yield, and selectivities for C<sub>2</sub> and coke) with an error of < 5%. Trained ML models are applied to maximize the C<sub>2</sub> yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C<sub>2</sub> yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.<br>

scholarly journals Reaction Condition Optimization for Non-Oxidative Conversion of Methane Using Artificial Intelligence

2020 ◽  
Author(s):  
Hyun Woo Kim ◽  
Sung Woo Lee ◽  
Gyoung S. Na ◽  
Seung Ju Han ◽  
Seok Ki Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Chemical Reaction ◽  
Coke Formation ◽  
Oxidative Conversion ◽  
Reaction Conditions ◽  
Condition Optimization ◽  
Controllable Factors ◽  
Conversion Of Methane ◽  
Oxidative Conversion Of Methane

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C<sub>2</sub> compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH<sub>4</sub> conversion, C<sub>2</sub> yield, and selectivities for C<sub>2</sub> and coke) with an error of < 5%. Trained ML models are applied to maximize the C<sub>2</sub> yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C<sub>2</sub> yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.<br>

Biomimetic reductive amination under the continuous-flow reaction conditions

2010 ◽  
Vol 131 (2) ◽  
pp. 261-265 ◽  
Author(s):  
Vadim A. Soloshonok ◽  
Hector T. Catt ◽  
Taizo Ono
Keyword(s):  
Continuous Flow ◽  
Reductive Amination ◽  
Reaction Conditions ◽  
Flow Reaction

ChemInform Abstract: Biomimetic Reductive Amination under the Continuous-Flow Reaction Conditions.

ChemInform ◽  
2010 ◽  
Vol 41 (24) ◽  
pp. no-no
Author(s):  
Vadim A. Soloshonok ◽  
Hector T. Catt ◽  
Taizo Ono
Keyword(s):  
Continuous Flow ◽  
Reductive Amination ◽  
Reaction Conditions ◽  
Flow Reaction

Pd catalysts supported on dual-pore monolithic silica beads for chemoselective hydrogenation under batch and flow reaction conditions

2020 ◽  
Vol 10 (18) ◽  
pp. 6359-6367
Author(s):  
Tsuyoshi Yamada ◽  
Aya Ogawa ◽  
Hayato Masuda ◽  
Wataru Teranishi ◽  
Akiko Fujii ◽  
...  
Keyword(s):  
Palladium Catalysts ◽  
Reaction Conditions ◽  
Pd Catalysts ◽  
Monolithic Silica ◽  
Chemoselective Hydrogenation ◽  
Different Types ◽  
Silica Beads ◽  
Flow Reaction

Two different types of palladium catalysts supported on dual-pore monolithic silica beads [5% Pd/SM and 0.25% Pd/SM(sc)] for chemoselective hydrogenation were developed.

scholarly journals Highly Selective Hydrogenative Conversion of Nitriles into Tertiary, Secondary, and Primary Amines under Flow Reaction Conditions

ChemSusChem ◽  
2021 ◽  
Author(s):  
Tsuyoshi Yamada ◽  
Kwihwan Park ◽  
Chikara Furugen ◽  
Jing Jiang ◽  
Eisho Shimizu ◽  
...  
Keyword(s):  
Primary Amines ◽  
Reaction Conditions ◽  
Flow Reaction
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