领域(数学)
力场(虚构)
计算机科学
功能(生物学)
人工神经网络
量子化学
统计物理学
人工智能
分子
物理
量子力学
数学
进化生物学
纯数学
生物
作者
Jinzhe Zeng,Liqun Cao,Tong Zhu
出处
期刊:Elsevier eBooks
[Elsevier]
日期:2022-09-24
卷期号:: 279-294
被引量:3
标识
DOI:10.1016/b978-0-323-90049-2.00001-9
摘要
Recently, artificial neural network-based methods for the construction of potential energy surfaces and molecular dynamics (MD) simulations based on them have been increasingly used in the field of theoretical chemistry. The neural network potentials (NNP) strike a good balance between accuracy and computational efficiency relative to quantum chemical calculations and MD simulations based on classical force fields. Thus, NNP is becoming a powerful tool for studying the structure and function of molecules. In this chapter, we introduce the basic theory of NNP. The construction steps and the usage of NNP are also introduced in detail with the MD simulation of methane combustion as an example. We hope that this chapter can help those readers who are new but interested in entering this field.
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