Shotgun crystal structure prediction using machine-learned formation energies

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Liu Chang, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Artem R. Oganov, Ryo Yoshida
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Abstract

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.

Abstract Image

利用机器学习的地层能量预测散弹枪晶体结构
通过在广泛的原子构型空间中找到能量表面的全局或局部最小值,可以预测组装原子的稳定或亚稳晶体结构。一般来说,这需要重复的第一性原理能量计算,这对于大型晶体系统通常是不切实际的。在这里,我们在解决晶体结构预测问题方面取得了重大进展:我们使用带有机器学习能量预测器的虚拟创建的大型晶体结构库进行了非迭代的单次筛选。这种散弹法(ShotgunCSP)有两个关键技术组成部分:用于精确预测预松弛晶体状态能量的迁移学习,以及基于元素取代和对称限制结构生成的两个生成模型,以产生有前途的多样化晶体结构。第一性原理计算仅用于生成训练样本和改进一些选定的预松弛晶体结构。与传统方法相比,ShotunCSP方法的计算强度更小,并且在90种不同晶体结构的基准测试中显示出优异的预测精度,达到93.3%。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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