Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
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DOI：
10.1103/physrevb.99.064114
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发表时间：
2019-02-27
影响因子：
3.7
通讯作者：
Oganov, Artem R.
中科院分区：
文献类型：
--
作者：
Podryabinkin, Evgeny, V;Tikhonov, Evgeny, V;Oganov, Artem R.
提出了一种基于进化算法USPEX和机器学习动态学习原子间相互作用势的晶体结构预测方法。我们的方法允许从头开始自动构建原子间相互作用模型，取代昂贵的密度泛函理论(DFT)，并提供几个数量级的加速比。然后对预测的低能结构进行DFT测试，确保我们的机器学习模型不会引入任何预测误差。我们测试了我们的方法对碳、钠的高压相和硼的同素异形体的晶体结构的预测，包括那些在原始细胞中有超过100个原子的同素异形体。所有主要的同素异形体都已被复制，并用非常有限的计算工作预测了迄今未知的54原子的硼结构。
We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on prediction of crystal structures of carbon, high-pressure phases of sodium, and boron allotropes, including those that have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced, and a hitherto unknown 54-atom structure of boron has been predicted with very modest computational effort.