scholarly journals Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning

ChemCatChem ◽  
2020 ◽  
Author(s):  
Kanami Sugiyama ◽  
Thanh Nhat Nguyen ◽  
Sunao Nakanowatari ◽  
Itsuki Miyazato ◽  
Toshiaki Taniike ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
Direct Design

scholarly journals Constructing catalyst knowledge networks from catalyst big data in oxidative coupling of methane for designing catalysts

2021 ◽  
Author(s):  
Lauren Takahashi ◽  
Thanh Nhat Nguyen ◽  
Sunao Nakanowatari ◽  
Aya Fujiwara ◽  
Toshiaki Taniike ◽  
...  
Keyword(s):  
Big Data ◽  
High Throughput ◽  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
Catalyst Design ◽  
Knowledge Networks ◽  
New Method ◽  
High Throughput Experimentation

Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.

High-Throughput Experimentation and Catalyst Informatics for Oxidative Coupling of Methane

ACS Catalysis ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 921-932 ◽  
Author(s):  
Thanh Nhat Nguyen ◽  
Thuy Tran Phuong Nhat ◽  
Ken Takimoto ◽  
Ashutosh Thakur ◽  
Shun Nishimura ◽  
...  
Keyword(s):  
High Throughput ◽  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
High Throughput Experimentation

Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

2021 ◽  
Author(s):  
Junya Ohyama ◽  
Takaaki Kinoshita ◽  
Eri Funada ◽  
Hiroshi Yoshida ◽  
Masato Machida ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Low Temperature ◽  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
Direct Design

Direct design of low temperature oxidative coupling of methane catalysts is proposed via machine learning and data mining.

Multidimensional Classification of Catalysts in Oxidative Coupling of Methane through Machine Learning and High-Throughput Data

2020 ◽  
Vol 11 (16) ◽  
pp. 6819-6826
Author(s):  
Keisuke Takahashi ◽  
Lauren Takahashi ◽  
Thanh Nhat Nguyen ◽  
Ashutosh Thakur ◽  
Toshiaki Taniike
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
High Throughput Data

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

scholarly journals Identification of the structure of the Bi promoted Pt non-oxidative coupling of methane catalyst: a nanoscale Pt3Bi intermetallic alloy

2019 ◽  
Vol 9 (6) ◽  
pp. 1349-1356 ◽  
Author(s):  
Johnny Zhu Chen ◽  
Zhenwei Wu ◽  
Xiaoben Zhang ◽  
Slgi Choi ◽  
Yang Xiao ◽  
...  
Keyword(s):  
Oxidative Coupling ◽  
Oxidative Coupling Of Methane ◽  
Intermetallic Alloy ◽  
Alloy Catalyst

Identification of a Pt3Bi nanoscale, surface intermetallic alloy catalyst for non-oxidative coupling of methane (NOCM).

A deep learning-based concept for high throughput image flow cytometry

2021 ◽  
Vol 118 (12) ◽  
pp. 123701
Author(s):  
Julie Martin-Wortham ◽  
Steffen M. Recktenwald ◽  
Marcelle G. M. Lopes ◽  
Lars Kaestner ◽  
Christian Wagner ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Deep Learning ◽  
High Throughput ◽  
Image Flow
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