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Machine Learning For Molecular Thermodynamics Jiaqi Ding Nan Xu Manh Tien Nguyen Qi Qiao Yao Shi Yi He Qing Shao

Name: Machine Learning For Molecular Thermodynamics Jiaqi Ding Nan Xu Manh Tien Nguyen Qi Qiao Yao Shi Yi He Qing Shao
SKU: BELL-47667738
Price: 31.00 USD
Availability: InStock
Rating: 5 (80 reviews)

SKU: BELL-47667738

$ 31.00 ~~$ 45.00~~ (-31%)

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Machine Learning For Molecular Thermodynamics Jiaqi Ding Nan Xu Manh Tien Nguyen Qi Qiao Yao Shi Yi He Qing Shao instant download after payment.

Publisher: The Chemical Industry and Engineering Society of China, and Chemical Industry Press.

File Extension: PDF

File size: 2.67 MB

Pages: 13

Author: Jiaqi Ding & Nan Xu & Manh Tien Nguyen & Qi Qiao & Yao Shi & Yi He & Qing Shao

Language: English

Year: 2021

Product desciption

Machine Learning For Molecular Thermodynamics Jiaqi Ding Nan Xu Manh Tien Nguyen Qi Qiao Yao Shi Yi He Qing Shao by Jiaqi Ding & Nan Xu & Manh Tien Nguyen & Qi Qiao & Yao Shi & Yi He & Qing Shao instant download after payment.

This is not a book; it is a 13 page published article (in English) from the
Chinese Journal of Chemical Engineering, 31 (2021) 227-239. doi:10.1016/j.cjche.2020.10.044a b s t r a c t
Thermodynamic properties of complex systems play an essential role in developing chemical engineering
processes. It remains a challenge to predict the thermodynamic properties of complex systems in a wide
range and describe the behavior of ions and molecules in complex systems. Machine learning emerges as
a powerful tool to resolve this issue because it can describe complex relationships beyond the capacity of
traditional mathematical functions. This minireview will summarize some fundamental concepts of
machine learning methods and their applications in three aspects of the molecular thermodynamics
using several examples. The first aspect is to apply machine learning methods to predict the thermody-
namic properties of a broad spectrum of systems based on known data. The second aspect is to integer
machine learning and molecular simulations to accelerate the discovery of materials. The third aspect
is to develop machine learning force field that can eliminate the barrier between quantum mechanics
and all-atom molecular dynamics simulations. The applications in these three aspects illustrate the
potential of machine learning in molecular thermodynamics of chemical engineering. We will also dis-
cuss the perspective of the broad applications of machine learning in chemical engineering.
Ó2021 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd.