Accelerating crystal structure search through active learning with neural networks for rapid relaxations
arxiv(2024)
Abstract
Global optimization of crystal compositions is a significant yet
computationally intensive method to identify stable structures within chemical
space. The specific physical properties linked to a three-dimensional atomic
arrangement make this an essential task in the development of new materials. We
present a method that efficiently uses active learning of neural network force
fields for structure relaxation, minimizing the required number of steps in the
process. This is achieved by neural network force fields equipped with
uncertainty estimation, which iteratively guide a pool of randomly generated
candidates towards their respective local minima. Using this approach, we are
able to effectively identify the most promising candidates for further
evaluation using density functional theory (DFT). Our method not only reliably
reduces computational costs by up to two orders of magnitude across the
benchmark systems Si16 , Na8Cl8 , Ga8As8 and Al4O6 , but also excels in finding
the most stable minimum for the unseen, more complex systems Si46 and Al16O24 .
Moreover, we demonstrate at the example of Si16 that our method can find
multiple relevant local minima while only adding minor computational effort.
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