We focus on the potential possibilities for supporting Scanning Probe
Microscopy measurements, emphasizing the application of Artificial
Intelligence, especially Machine Learning as well as quantum computing. It
turned out that Artificial Intelligence can be helpful in the experimental
processes automation in routine operations, the algorithmic search for good
sample regions, and shed light on the structure property relationships. Thus,
it contributes to increasing the efficiency and accuracy of optical nanoscopy
scanning probes. Moreover, the combination of Artificial Intelligence based
algorithms and quantum computing may have a huge potential to increase the
practical application of Scanning Probe Microscopy. The limitations were also
discussed. Finally, we outline a research path for the improvement of the
proposed approach.

我们重点关注于支持扫描探针显微测量的潜在可能性，强调了应用人工智能，特别是机器学习和量子计算。结果表明，人工智能在实验过程自动化中可以提供帮助，算法搜索良好样本区域，并阐明了结构性质关系的一些见解。因此，它有助于提高光学纳米显微扫描探针的效率和准确性。此外，基于人工智能算法和量子计算的结合可能对扫描探针显微的实际应用有巨大的潜力。文章还讨论了一些局限性，并概述了改进所提方法的研究路径。

扫描探针显微镜如何受到人工智能和量子计算的支持

How scanning probe microscopy can be supported by Artificial  Intelligence and quantum computing

The revolution in materials in the past century was built on a knowledge of
the atomic arrangements and the structure-property relationship. The sine qua
non for obtaining quantitative structural information is single crystal
crystallography. However, increasingly we need to solve structures in cases
where the information content in our input signal is significantly degraded,
for example, due to orientational averaging of grains, finite size effects due
to nanostructure, and mixed signals due to sample heterogeneity. Understanding
the structure property relationships in such situations is, if anything, more
important and insightful, yet we do not have robust approaches for
accomplishing it. In principle, machine learning (ML) and deep learning (DL)
are promising approaches since they augment information in the degraded input
signal with prior knowledge learned from large databases of already known
structures. Here we present a novel ML approach, a variational query-based
multi-branch deep neural network that has the promise to be a robust but
general tool to address this problem end-to-end. We demonstrate the approach on
computed powder x-ray diffraction (PXRD), along with partial chemical
composition information, as input. We choose as a structural representation a
modified electron density we call the Cartesian mapped electron density (CMED),
that straightforwardly allows our ML model to learn material structures across
different chemistries, symmetries and crystal systems. When evaluated on
theoretically simulated data for the cubic and trigonal crystal systems, the
system achieves up to $93.4\%$ average similarity with the ground truth on
unseen materials, both with known and partially-known chemical composition
information, showing great promise for successful structure solution even from
degraded and incomplete input data.

介绍了一种新颖的机器学习方法，使用多分支深度神经网络和计算的粉末 X 射线衍射，结合部分化学组成信息，实现对结构的成功求解。