A multi-task deep learning neural network for predicting flammability-related properties from molecular structures Article Swipe

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Ao Yang , Yang Su , Zihao Wang , Saimeng Jin , Jingzheng Ren , Xiangping Zhang , Weifeng Shen , James H. Clark ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1039/d1gc00331c

It is significant that hazardous properties of chemicals including replacements for banned or restricted products are assessed at an early stage of product and process design.

Related Topics

Combustibility And Flammability

Hazardous Waste

Process (Computing)

Computer Science

Artificial Intelligence

Concepts

Flammability Task (project management) Hazardous waste Artificial neural network Process (computing) Product (mathematics) Computer science Artificial intelligence Process engineering Biochemical engineering Machine learning Engineering Chemistry Systems engineering Waste management Organic chemistry Mathematics Geometry Operating system

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Type: article
Language: en
Landing Page: https://doi.org/10.1039/d1gc00331c
OA Status: green
Cited By: 28
References: 51
Related Works: 10
OpenAlex ID: https://openalex.org/W3160026094

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DOI: https://doi.org/10.1039/d1gc00331c

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Title: A multi-task deep learning neural network for predicting flammability-related properties from molecular structures

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-01-01

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Authors: Ao Yang, Yang Su, Zihao Wang, Saimeng Jin, Jingzheng Ren, Xiangping Zhang, Weifeng Shen, James H. Clark

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Landing page: https://doi.org/10.1039/d1gc00331c

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OA URL: https://ir.ipe.ac.cn/handle/122111/48983

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Concepts: Flammability, Task (project management), Hazardous waste, Artificial neural network, Process (computing), Product (mathematics), Computer science, Artificial intelligence, Process engineering, Biochemical engineering, Machine learning, Engineering, Chemistry, Systems engineering, Waste management, Organic chemistry, Mathematics, Geometry, Operating system

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Cited by: 28

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