人工神经网络
计算机科学
人工智能
三元运算
催化作用
机器学习
纳米技术
化学
材料科学
生物化学
程序设计语言
作者
Sicong Ma,Pei‐Lin Kang,Cheng Shang,Zhi‐Pan Liu
出处
期刊:The Royal Society of Chemistry eBooks
[The Royal Society of Chemistry]
日期:2020-07-15
卷期号:: 488-511
被引量:4
标识
DOI:10.1039/9781839160233-00488
摘要
While the potential energy surface (PES) determines the physicochemical properties of matter, chemical system surfaces are often too complex to solve even with modern computing facilities. Heterogeneous catalysis, being widely utilized in industry, calls for new techniques and methods to resolve the active site structure and reaction intermediates at the atomic scale. In this chapter, we provide an overview of recent theoretical progress on large-scale atomistic simulation via the machine learning global neural network (G-NN) potential developed by our research group in recent years, focusing on methodology and representative applications in heterogeneous catalysis. The combination of global optimization and machine learning provides a convenient and automated way to generate the transferable and robust G-NN potential, which can be utilized to reveal new chemistry from unknown regions of the PES at an affordable computational cost. The predictive power of the G-NN potential is demonstrated in several examples, where the method is applied to explore the material crystal phases and the structure of supported catalysts, to follow surface structure evolution under high-pressure hydrogen and to determine the ternary oxide phase diagram. Limitations and future directions of the G-NN potential method are also discussed.
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