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WIREs Comput Mol Sci
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LASP: Fast global potential energy surface exploration

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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
Architecture and the modular map of LASP code
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The energy‐OP6 contour map for the β‐B minima from the SSW global optimization using the NN PES. OP6 is the distance‐weighted Steinhardt order parameter with degree set to 6, and the density of states is indicated by color. The energy of β‐B global minimum is set as zero. The red box indicates the boundary above which the B28 cages start to melt. The inset shows a typical structural fragment of β‐B, including B12 (red) and B28 (green) cages, and important partial occupied sites, that is, B16 (green ball), B19 (yellow ball), B20 (purple ball)
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Volume versus temperature plot from molecular dynamics simulation of the melting (blue) and refreezing (orange) of Al metal. A complete hysteresis of volume forms during the continuous heating‐cooling process. The transition temperatures T+ is 1,100 K and T is 700 K
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(a) Comparison of the computation time for a 28‐atom boron crystal using NN and DFT with plane wave basis set (PW DFT). The x axis is the logarithm of time in the unit of seconds. The inset shows the structure of the boron crystal. (b) Speed‐up of G‐NN computation of boron crystals. The x axis (Np) is the number of processors and the y axis is the speed‐up. (Copyright: 2018, John Wiley and Sons)
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Scheme of the parallel computation hierarchy in the PTSD based HDNN. The total energy of a system (blue) is a sum over all atomic energy (green). The atomic energy involves descriptor computation (orange) and an NN evaluation (white). The descriptor computation includes contributions from each n‐body function (group unit) computation (red). (Copyright: 2018, John Wiley and Sons)
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Pseudocode for performing a SSW step in LASP code written mainly in Fortran 90. For a SSW simulation, the energy and forces of the current structure (update_energy_force) is first required. Five sequential steps are taken according to the SSW status: (“NewStart”) step is to generate a random movement direction; (“CBD”) step is to soften the movement direction by using the constrained Broyden dimer with bias potential method; (“moveds”) step is to extrapolate atoms/cells coordinates along the direction with the step size of a Gaussian ds; (“Climb”) step is to locally relax the structure on the modified PES with added bias potentials; (“Allopt”) is to perform the fully geometry relaxation and finally reach a new minimum
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