Annealing for prediction of grand canonical crystal structures: Efficient implementation of n-body atomic interactions
Yannick Couzinie, Yusuke Nishiya, Hirofumi Nishi, Taichi Kosugi,, Hidetoshi Nishimori, Yu-ichiro Matsushita

TL;DR
This paper introduces an annealing-based method for predicting crystal structures by modeling n-body atomic interactions, including covalent bonds, using QUBO/HUBO formulations solved via simulated and quantum annealing.
Contribution
It presents an efficient implementation of n-body interactions in annealing schemes for crystal structure prediction, including a reduction scheme for higher-order terms and simultaneous optimization of density and structure.
Findings
Successfully predicts covalently bonded crystal structures.
Reproduces ground states with high probability.
Reduces complexity by not including target atom number explicitly.
Abstract
We propose an annealing scheme usable on modern Ising machines for crystal structures prediction (CSP) by taking into account the general n-body atomic interactions, and in particular three-body interactions which are necessary to simulate covalent bonds. The crystal structure is represented by discretizing a unit cell and placing binary variables which express the existence or non-existence of an atom on every grid point. The resulting quadratic unconstrained binary optimization (QUBO) or higher-order unconstrained binary optimization (HUBO) problems implement the CSP problem and is solved using simulated and quantum annealing. Using the example of Lennard-Jones clusters we show that it is not necessary to include the target atom number in the formulation allowing for simultaneous optimization of both the particle density and the configuration and argue that this is advantageous for…
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Taxonomy
TopicsMachine Learning in Materials Science · Topic Modeling · Protein Structure and Dynamics
