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arXiv 2401.14564cond-mat.mes-hall
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推进扫描探针显微镜模拟:探针-粒子模型十年发展

Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models

Niko Oinonen, Aliaksandr V. Yakutovich, Aurelio Gallardo, Martin Ondracek, Prokop Hapala, Ondrej Krejci

AI总结:

本文综述了探针-粒子模型十年发展,介绍开源ppafm软件包最新版在精度、计算性能和易用性上的提升,并展示其在nc-AFM模拟及机器学习原子结构恢复等应用中的广泛价值。

摘要:

探针-粒子模型(Probe-Particle Model)汇集了为扫描探针显微镜实验模拟而设计的多种理论,采用非反应性、柔性针尖顶点以实现亚分子级分辨率。本文介绍了开源 ppafm 软件包中实现的最新版探针-粒子模型,重点展示了其在精度、计算性能和用户友好性方面的显著进步。为证明这一点,我们对非接触式原子力显微镜(nc-AFM)的模拟方法进行了全面综述。这些方法复杂度各异,从简单的 Lennard-Jones 势到最新的全密度基模型。我们将这些方法与从头计算(ab initio)参考结果进行了比较,展示了各自的优点。通过使用 OpenMP 和 OpenCL 技术,ppafm 软件包的所有部分都实现了 1-2 个数量级的加速。更新后的软件包包含交互式图形用户界面,并通过 pip 无缝集成到 Python 生态系统中,便于高级脚本编写以及与其他软件的互操作。这种适应性使 ppafm 成为高通量应用的理想工具,包括训练机器学习模型以从 nc-AFM 测量中自动恢复原子结构。我们预见该应用在未来单分子分析、合成以及表面科学的整体发展中具有巨大潜力。此外,我们还讨论了其他亚分子级扫描探针成像技术的模拟,如键分辨扫描隧道显微镜(BR-STM)和开尔文探针力显微镜(KPFM),这些模拟均建立在探针-粒子模型的稳健基础之上。总之,这证明了该模型在表面科学和分子化学多个领域的广泛影响。

英文摘要:

The Probe-Particle Model combine theories designed for the simulation of scanning probe microscopy experiments, employing non-reactive, flexible tip apices to achieve sub-molecular resolution. In the article we present the latest version of the Probe-Particle Model implemented in the open-source ppafm package, highlighting substantial advancements in accuracy, computational performance, and userfriendliness. To demonstrate this we provide a comprehensive review of approaches for simulating non-contact Atomic Force Microscopy. They vary in complexity from simple Lennard-Jones potential to the latest full density-based model. We compared those approaches with ab initio calculated references, showcasing their respective merits. All parts of the ppafm package have undergone acceleration by 1-2 orders of magnitude using OpenMP and OpenCL technologies. The updated package includes n interactive graphical user interface and seamless integration into the Python ecosystem via pip, facilitating advanced scripting and interoperability with other software. This adaptability positions ppafm as an ideal tool for high-throughput applications, including the training of machine learning models for the automatic recovery of atomic structures from nc-AFM measurements. We envision significant potential for this application in future single-molecule analysis, synthesis, and advancements in on-surface science in general. Additionally, we discuss simulations of other sub-molecular scanning-probe imaging techniques, such as bond-resolved scanning tunnelling microscopy and kelvin probe force microscopy, all built on the robust foundation of the Probe-Particle Model. Altogether this demonstrates the broad impact of the model across diverse domains of surface science and molecular chemistry.

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