Abstract
Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures. Relaxing a structure requires multiple optimization steps. It is time consuming to fully relax all the initial structures, but it is difficult to figure out which initial structure leads to the optimal solution in advance. In this paper, we propose a optimization method for crystal structure prediction, called Look Ahead based on Quadratic Approximation, that optimally assigns optimization steps to each candidate structure. It allows us to identify the most stable structure with a minimum number of total local optimization steps. Our simulations using known systems Si, NaCl, Y2Co17, Al2O3, and GaAs showed that the computational cost can be reduced significantly compared to random search. This method can be applied for controlling all kinds of local optimizations based on first-principles calculations to obtain best results under restricted computational resources.
Highlights
In designing new crystalline materials, crystal structure prediction (CSP) for a given chemical composition is the most fundamental task
In the proposed method, called Look Ahead based on Quadratic Approximation (LAQA), we first generate a large number of candidate structures, select promising structures based on the energy estimation method, and proceed to calculate them preferentially
When performing CSP, if the calculation is judged insufficient, we may not find a stable structure, and need to perform additional calculation for newly generated candidate structures. To deal with such a procedure, we propose a method to increase the number of candidate structures gradually, called sequential LAQA
Summary
In designing new crystalline materials, crystal structure prediction (CSP) for a given chemical composition is the most fundamental task. In the proposed method, called Look Ahead based on Quadratic Approximation (LAQA), we first generate a large number of candidate structures, select promising structures based on the energy estimation method, and proceed to calculate them preferentially. When performing CSP, if the calculation is judged insufficient, we may not find a stable structure, and need to perform additional calculation for newly generated candidate structures To deal with such a procedure, we propose a method to increase the number of candidate structures gradually, called sequential LAQA (sLAQA). We investigated how much of a computational cost reduction can be obtained with our proposed methods, compared with the widely used random searching approach, which repeatedly generate random structures and performs full local optimizations. We can reduce total local optimization steps by controlling each calculation of a structure based on the prediction of the relaxed energy of the structure b
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