Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery.,The Journal of Physical Chemistry Letters

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Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery.
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2020-09-21 , DOI: 10.1021/acs.jpclett.0c02357
Qunchao Tong _{1,

2} , Pengyue Gao ₁ , Hanyu Liu _{1,

3,

4} , Yu Xie _{1,

3} , Jian Lv _{1,

2} , Yanchao Wang _{1,

2} , Jijun Zhao ₅

Affiliation

The theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), based solely on chemical composition, has already become a routine tool to determine the structures of physical and chemical systems, e.g., solids and clusters. However, the application of DFT to more realistic simulations, to a large extent, is impeded because of the unfavorable scaling of the computational cost with respect to the system size. During recent years, the machine learning potential (MLP) method has been rapidly rising as an accurate and efficient tool for atomistic simulations. In this Perspective, we provide an introduction to the basic principles and advantages of the combination of structure prediction and MLP, as well as the challenges and opportunities associated with this promising approach.

中文翻译：

将机器学习潜力与结构预测相结合，以加快材料的设计和发现。

仅基于化学成分的通过量子力学原子模拟（例如密度泛函理论（DFT））的理论结构预测方法已经成为确定物理和化学系统（例如固体和团簇）结构的常规工具。但是，由于不利于计算成本相对于系统大小的缩放，在很大程度上限制了DFT在更现实的仿真中的应用。近年来，机器学习潜能（MLP）方法作为一种用于原子模拟的准确而有效的工具，正在迅速发展。在此观点中，我们将介绍结构预测和MLP相结合的基本原理和优势，以及与该有前途的方法相关的挑战和机遇。

更新日期：2020-10-16

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