Hydrogen diffusion in magnesium using machine learning potentials: a comparative study

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Andrea Angeletti, Luca Leoni, Dario Massa, Luca Pasquini, Stefanos Papanikolaou, Cesare Franchini
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Abstract

Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab-initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning. By considering different temperatures and concentration regimes, we obtain hydrogen diffusion coefficients and activation energy values which align remarkably well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force-field allows the retrieval of DFT-level accuracy at a fraction of the computational cost.

Abstract Image

氢在镁中的扩散使用机器学习电位:一项比较研究
由于氢缺陷和晶格之间复杂的相互作用,理解和准确预测材料中的氢扩散具有挑战性。这些相互作用跨越了大的长度和时间尺度,使得它们很难用标准的从头算技术来解决。这项工作通过主动学习采用加速机器学习(ML)分子动力学模拟来解决这一挑战。我们利用各种训练策略,如实时学习、预训练通用模型和微调,对不同的基于ml的原子间势方案(包括VASP、MACE和CHGNet)进行了比较研究。通过考虑不同的温度和浓度,我们得到的氢扩散系数和活化能值与实验结果非常吻合,强调了ml辅助方法在扩散动力学背景下的有效性和准确性。特别是,我们的程序显著减少了与传统的过渡态计算或特别设计的原子间势相关的计算工作量。结果突出了缺陷材料的预训练通用解决方案的局限性,以及如何通过微调来改进它们。具体来说,在VASP ML力场的实时训练过程中对数据库上的模型进行微调,可以以很小的计算成本检索dft级别的精度。
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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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