Prescreening candidate structures with reliable classical
potentials
is an effective way to accelerate ab initio ground
state searches. Given the growing popularity of machine learning force
fields, surprisingly little work has been dedicated to quantifying
their advantages over traditional potentials in global structure optimizations.
In this study, we have developed a neural network (NN) model and systematically
benchmarked it against a commonly used Gupta potential and an embedded
atom model in the search for stable Au
N
clusters (30 ≤ N ≤ 80). An efficient
simultaneous optimization of clusters in the full size range was achieved
with our recently introduced multitribe evolutionary algorithm. Density
functional theory (DFT) evaluations of candidate configurations identified
with the three classical models revealed that the NN structures were
lower in energy by at least 10 meV/atom for 30 of the 51 sizes. We
also demonstrated that DFT evaluation of all NN-relaxed structures during evolutionary searches resulted in finding even more
stable configurations, which highlights the need for further improvement
of the NN accuracy to avoid excessive DFT calculations. Overall, the
global searches produced putative ground states with matching or lower
DFT energies compared to all previously reported Au clusters with
30–80 atoms.
The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified an additional stable Li3Sn phase with a large BCC-based hR48 structure and a possible high-T LiSn4 ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, low-symmetry 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine-learning interatomic potentials hold for accelerating ab initio prediction of complex materials.
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