A major challenge in materials science is the determination of the structure
of nanometer sized objects. Here we present a novel approach that uses a
generative machine learning model based on a Diffusion model that is trained on
45,229 known structures. The model factors both the measured diffraction
pattern as well as relevant statistical priors on the unit cell of atomic
cluster structures. Conditioned only on the chemical formula and the
information-scarce finite-size broadened powder diffraction pattern, we find
that our model, PXRDnet, can successfully solve simulated nanocrystals as small
as 10 angstroms across 200 materials of varying symmetry and complexity,
including structures from all seven crystal systems. We show that our model can
determine structural solutions with up to $81.5\%$ accuracy, as measured by
structural correlation. Furthermore, PXRDnet is capable of solving structures
from noisy diffraction patterns gathered in real-world experiments. We suggest
that data driven approaches, bootstrapped from theoretical simulation, will
ultimately provide a path towards determining the structure of previously
unsolved nano-materials.

利用基于扩散模型的生成式机器学习模型，我们能够成功解决模拟的小至 10 安培、包含来自七个晶体系统的各种对称性和复杂性的 200 种材料的纳米晶体结构，并且能够处理真实实验中噪声扩散模式收集的结构解决方案，这表明数据驱动的方法将提供解决以前未解决的纳米材料结构的途径。

扩散模型在纳米晶粉末衍射数据的从头结构解析中表现出潜力

Diffusion Models Are Promising for Ab Initio Structure Solutions from  Nanocrystalline Powder Diffraction Data

The Event Horizon Telescope (EHT) provides an avenue to study black hole
accretion flows on event-horizon scales. Fitting a semi-analytical model to EHT
observations requires the construction of synthetic images, which is
computationally expensive. This study presents an image generation tool in the
form of a generative machine learning model, which extends the capabilities of
a variational autoencoder. This tool can rapidly and continuously interpolate
between a training set of images and can retrieve the defining parameters of
those images. Trained on a set of synthetic black hole images, our tool
showcases success in both interpolating black hole images and their associated
physical parameters. By reducing the computational cost of generating an image,
this tool facilitates parameter estimation and model validation for
observations of black hole system.

该研究提出了一种生成式机器学习模型作为图像生成工具，能够快速地插值黑洞图像和其相关的物理参数，从而降低生成图像的计算成本，为黑洞系统的参数估计和模型验证提供便利。

图像参数和参数混编学习

Autoencoding Labeled Interpolator, Inferring Parameters From Image, And  Image From Parameters

We present the OGAN algorithm for automatic requirement falsification of
cyber-physical systems. System inputs and output are represented as piecewise
constant signals over time while requirements are expressed in signal temporal
logic. OGAN can find inputs that are counterexamples for the safety of a system
revealing design, software, or hardware defects before the system is taken into
operation. The OGAN algorithm works by training a generative machine learning
model to produce such counterexamples. It executes tests atomically and does
not require any previous model of the system under test. We evaluate OGAN using
the ARCH-COMP benchmark problems, and the experimental results show that
generative models are a viable method for requirement falsification. OGAN can
be applied to new systems with little effort, has few requirements for the
system under test, and exhibits state-of-the-art CPS falsification efficiency
and effectiveness.

OGAN 算法通过训练生成式机器学习模型，可以自动对物理系统的需求进行伪造，为系统的安全性揭示设计、软件或硬件缺陷，并在系统投入运行之前发现。该算法使用原子测试执行，无需先前模型的支持，通过在 ARCH-COMP 基准问题上的评估结果表明，生成式模型是一种可行的需求伪造方法。OGAN 不仅适用于新系统，并且对系统的要求很少，具有最先进的 CPS 伪造效率和效果。