Charraud J-B, Geneste G, Torrent M, Maillet J-B
CEA-DAM, DIF, F-91297 Arpajon Cedex, France.
J Chem Phys. 2022 May 28;156(20):204102. doi: 10.1063/5.0085173.
The search for new superhydrides, promising materials for both hydrogen storage and high temperature superconductivity, made great progress, thanks to atomistic simulations and Crystal Structure Prediction (CSP) algorithms. When they are combined with Density Functional Theory (DFT), these methods are highly reliable and often match a great part of the experimental results. However, systems of increasing complexity (number of atoms and chemical species) become rapidly challenging as the number of minima to explore grows exponentially with the number of degrees of freedom in the simulation cell. An efficient sampling strategy preserving a sustainable computational cost then remains to be found. We propose such a strategy based on an active-learning process where machine learning potentials and DFT simulations are jointly used, opening the way to the discovery of complex structures. As a proof of concept, this method is applied to the exploration of tin crystal structures under various pressures. We showed that the α phase, not included in the learning process, is correctly retrieved, despite its singular nature of bonding. Moreover, all the expected phases are correctly predicted under pressure (20 and 100 GPa), suggesting the high transferability of our approach. The method has then been applied to the search of yttrium superhydrides (YH) crystal structures under pressure. The YH structure of space group Im-3m is successfully retrieved. However, the exploration of more complex systems leads to the appearance of a large number of structures. The selection of the relevant ones to be included in the active learning process is performed through the analysis of atomic environments and the clustering algorithm. Finally, a metric involving a distance based on x-ray spectra is introduced, which guides the structural search toward experimentally relevant structures. The global process (active-learning and new selection methods) is finally considered to explore more complex and unknown YH phases, unreachable by former CSP algorithms. New complex phases are found, demonstrating the ability of our approach to push back the exponential wall of complexity related to CSP.
寻找新型超氢化物（有望用于储氢和高温超导的材料）取得了重大进展，这得益于原子模拟和晶体结构预测（CSP）算法。当这些方法与密度泛函理论（DFT）相结合时，它们高度可靠，并且常常能与很大一部分实验结果相匹配。然而，随着系统复杂度的增加（原子和化学物种的数量），由于模拟单元中需要探索的极小值数量随自由度数量呈指数增长，这迅速成为了挑战。因此，仍有待找到一种能保持可持续计算成本的高效采样策略。我们基于主动学习过程提出了这样一种策略，其中机器学习势和DFT模拟被联合使用，为发现复杂结构开辟了道路。作为概念验证，该方法被应用于探索不同压力下的锡晶体结构。我们表明，尽管α相具有独特的键合性质且未包含在学习过程中，但仍能被正确找回。此外，在压力（20和100 GPa）下所有预期的相都被正确预测，这表明我们的方法具有很高的可转移性。然后该方法被应用于搜索压力下钇超氢化物（YH）的晶体结构。成功找回了空间群为Im-3m的YH结构。然而，对更复杂系统的探索导致出现了大量结构。通过分析原子环境和聚类算法来选择要纳入主动学习过程的相关结构。最后，引入了一种基于X射线光谱的距离度量，它引导结构搜索朝着与实验相关的结构进行。最终考虑采用全局过程（主动学习和新的选择方法）来探索以前的CSP算法无法触及的更复杂和未知的YH相。发现了新的复杂相，证明了我们的方法有能力突破与CSP相关的复杂度指数壁垒。
