scholarly journals Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network

2021 ◽  
Vol 125 (4) ◽  
pp. 1082-1092
Author(s):  
Weiqi Ji ◽  
Sili Deng
Keyword(s):  
Neural Network ◽  
Chemical Reaction ◽  
Reaction Pathways ◽  
Autonomous Discovery

scholarly journals Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinzhe Zeng ◽  
Liqun Cao ◽  
Mingyuan Xu ◽  
Tong Zhu ◽  
John Z. H. Zhang
Keyword(s):  
Neural Network ◽  
Molecular Dynamics ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Molecular Species ◽  
Md Simulation ◽  
Dynamics Simulation ◽  
Reaction Pathways ◽  
Complex Reaction ◽  
Reaction Processes

Abstract Combustion is a complex chemical system which involves thousands of chemical reactions and generates hundreds of molecular species and radicals during the process. In this work, a neural network-based molecular dynamics (MD) simulation is carried out to simulate the benchmark combustion of methane. During MD simulation, detailed reaction processes leading to the creation of specific molecular species including various intermediate radicals and the products are intimately revealed and characterized. Overall, a total of 798 different chemical reactions were recorded and some new chemical reaction pathways were discovered. We believe that the present work heralds the dawn of a new era in which neural network-based reactive MD simulation can be practically applied to simulating important complex reaction systems at ab initio level, which provides atomic-level understanding of chemical reaction processes as well as discovery of new reaction pathways at an unprecedented level of detail beyond what laboratory experiments could accomplish.

scholarly journals Neural Network for Modeling of Chemical Reaction Systems: Kinetics of Concentrated Acid Hydrolysis of Walnut Green Skin

2013 ◽  
Vol 25 (4) ◽  
pp. 1793-1799 ◽  
Author(s):  
A. Arasteh Nodeh ◽  
M. Ardjmand ◽  
M.A. Fanaei ◽  
A.A. Safekordi
Keyword(s):  
Neural Network ◽  
Acid Hydrolysis ◽  
Chemical Reaction ◽  
Reaction Systems ◽  
Chemical Reaction Systems ◽  
Kinetics Of ◽  
Hydrolysis Of

Chemical Reaction Pathways for MOVPE Growth of Aluminum Nitride

2015 ◽  
Vol 5 (2) ◽  
pp. P73-P75 ◽  
Author(s):  
Yumi Inagaki ◽  
Takahiro Kozawa
Keyword(s):  
Aluminum Nitride ◽  
Chemical Reaction ◽  
Reaction Pathways ◽  
Movpe Growth

Graphical Instructions for Procedures Involving Material Flow

2018 ◽  
Vol 62 (1) ◽  
pp. 1803-1807
Author(s):  
Zachary H. Pugh ◽  
Douglas J. Gillan
Keyword(s):  
Chemical Reaction ◽  
Material Flow ◽  
Unique Feature ◽  
Reaction Pathways ◽  
Virtual Laboratory ◽  
Viewing Time ◽  
Abstract Object ◽  
Single Product ◽  
Multiple Products ◽  
Study Participants

Certain tasks, such as cooking and chemistry lab work, involve the transformation, combination, and separation of materials. Because these tasks are concerned with material flow, they can be represented using a type of formal diagram commonly used in chemistry and biology to represent chemical reaction pathways. A unique feature of this notation is its graphical depiction of changes in partonomy—how certain materials combine to form a single product, or how a material is decomposed into multiple products. In the present study, participants performed a series of abstract object transformation tasks in a virtual laboratory according to different instruction formats (text, semi diagram, full diagram). Each participant tested the three formats, each with one of three unique tasks. Differences in viewing time address whether the graphical depiction of partonomic change is advantageous for the user.

On the deduction of chemical reaction pathways from measurements of time series of concentrations

2001 ◽  
Vol 11 (1) ◽  
pp. 108 ◽  
Author(s):  
Michael Samoilov ◽  
Adam Arkin ◽  
John Ross
Keyword(s):  
Time Series ◽  
Chemical Reaction ◽  
Reaction Pathways
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