LASP: Fast global potential energy surface exploration

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 b...

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Vydané v:	Wiley interdisciplinary reviews. Computational molecular science Ročník 9; číslo 6; s. e1415 - n/a
Hlavní autori:	Huang, Si‐Da, Shang, Cheng, Kang, Pei‐Lin, Zhang, Xiao‐Jie, Liu, Zhi‐Pan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA Wiley Periodicals, Inc 01.11.2019 Wiley Subscription Services, Inc
Predmet:	Computer architecture Computer programs Computer simulation Exploration First principles Global optimization Interfaces LASP Molecular dynamics neural network Neural networks Potential energy potential energy surface Simulation Software SSW
ISSN:	1759-0876, 1759-0884
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Abstract	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. This article is categorized under: Software > Simulation Methods LASP is an atomistic simulation package targeted for solving the complex PES problems using the global neural network potentials.
AbstractList	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. This article is categorized under: Software > Simulation Methods LASP is an atomistic simulation package targeted for solving the complex PES problems using the global neural network potentials. 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. This article is categorized under: Software > Simulation Methods 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.This article is categorized under:Software > Simulation Methods
Author	Zhang, Xiao‐Jie Huang, Si‐Da Shang, Cheng Liu, Zhi‐Pan Kang, Pei‐Lin
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Snippet	Here we introduce the LASP code, which is designed for large‐scale atomistic simulation of complex materials with neural network (NN) potential. The software...
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SubjectTerms	Computer architecture Computer programs Computer simulation Exploration First principles Global optimization Interfaces LASP Molecular dynamics neural network Neural networks Potential energy potential energy surface Simulation Software SSW
Title	LASP: Fast global potential energy surface exploration
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