LASP: Fast global potential energy surface exploration,Wiley Interdisciplinary Reviews: Computational Molecular Science

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LASP: Fast global potential energy surface exploration
Wiley Interdisciplinary Reviews: Computational Molecular Science ( IF 16.8 ) Pub Date : 2019-03-10 , DOI: 10.1002/wcms.1415
Si‐Da Huang ₁ , Cheng Shang ₁ , Pei‐Lin Kang ₁ , Xiao‐Jie Zhang ₁ , Zhi‐Pan Liu ₁

Affiliation

Here we introduce the LASP code, which is designed for large‐scale atomistic simulation of complex materials with neural network (NN) potential. The software architecture and functionalities of LASP will be overviewed. LASP features with the global neural network (G‐NN) potential that is generated by learning the first principles dataset of global PES from stochastic surface walking (SSW) global optimization. The combination of the SSW method with global NN potential facilitates greatly the PES exploration for a wide range of complex materials. Not limited to SSW‐NN global optimization, the software implements standard interfaces to dock with other energy/force evaluation packages and can also perform common tasks for computing PES properties, such as single‐ended and double‐ended transition state search, the molecular dynamics simulation with and without restraints. A few examples are given to illustrate the efficiency and capabilities of LASP code. Our ongoing efforts for code developing and G‐NN potential library building are also presented.

中文翻译：

LASP：快速的全球势能面探索

在这里，我们介绍LASP代码，该代码旨在用于具有神经网络（NN）潜力的复杂材料的大规模原子模拟。将概述LASP的软件体系结构和功能。LASP具有全局神经网络（G-NN）的潜力，该潜力是通过从随机表面行走（SSW）全局优化中学习全局PES的第一个原理数据集而生成的。SSW方法与全局NN潜力的结合极大地促进了PES对各种复杂材料的探索。该软件不仅限于SSW-NN全局优化，它还实现了与其他能量/力评估包对接的标准接口，还可以执行用于计算PES属性的常见任务，例如单端和双端过渡状态搜索，有和没有约束的分子动力学模拟。给出了一些示例来说明LASP代码的效率和功能。还介绍了我们在代码开发和G-NN潜在库构建方面的持续努力。

更新日期：2019-07-05

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