Altermagnetism, a new magnetic phase, has been theoretically proposed and
experimentally verified to be distinct from ferromagnetism and
antiferromagnetism. Although altermagnets have been found to possess many
exotic physical properties, the very limited availability of known
altermagnetic materials~(e.g., 14 confirmed materials) hinders the study of
such properties. Hence, discovering more types of altermagnetic materials is
crucial for a comprehensive understanding of altermagnetism and thus
facilitating new applications in the next generation information technologies,
e.g., storage devices and high-sensitivity sensors. Here, we report 25 new
altermagnetic materials that cover metals, semiconductors, and insulators,
discovered by an AI search engine unifying symmetry analysis, graph neural
network pre-training, optimal transport theory, and first-principles electronic
structure calculation. The wide range of electronic structural characteristics
reveals that various innovative physical properties manifest in these newly
discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr
effect, and topological property. Noteworthy, we discovered 8 $i$-wave
altermagnetic materials for the first time. Overall, the AI search engine
performs much better than human experts and suggests a set of new altermagnetic
materials with unique properties, outlining its potential for accelerated
discovery of altermagnetic materials.

在这篇论文中，利用对称性分析、图神经网络预训练、最优传输理论和第一性原理电子结构计算，我们通过一个人工智能搜索引擎发现了涵盖金属、半导体和绝缘体的 25 种新型交替磁性材料，并发现了各种创新的物理性质，在加速发现交替磁性材料方面，人工智能搜索引擎表现优于人类专家。

AI 加速发现新型磁性材料

AI-accelerated Discovery of Altermagnetic Materials

Visual cues of enforcing bilaterally symmetric anatomies as normal findings
are widely used in clinical practice to disambiguate subtle abnormalities from
medical images. So far, inadequate research attention has been received on
effectively emulating this practice in CAD methods. In this work, we exploit
semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario,
i.e., anterior pelvic fracture detection in trauma PXRs, where semantically
pathological (refer to as fracture) and non-pathological (e.g., pose)
asymmetries both occur. Visually subtle yet pathologically critical fracture
sites can be missed even by experienced clinicians, when limited diagnosis time
is permitted in emergency care. We propose a novel fracture detection framework
that builds upon a Siamese network enhanced with a spatial transformer layer to
holistically analyze symmetric image features. Image features are spatially
formatted to encode bilaterally symmetric anatomies. A new contrastive feature
learning component in our Siamese network is designed to optimize the deep
image features being more salient corresponding to the underlying semantic
asymmetries (caused by pelvic fracture occurrences). Our proposed method have
been extensively evaluated on 2,359 PXRs from unique patients (the largest
study to-date), and report an area under ROC curve score of 0.9771. This is the
highest among state-of-the-art fracture detection methods, with improved
clinical indications.

本文提出一种新的骨盆前突破检测方法，基于对称性分析、空间转换层和 Siamese 网络，通过对像素信息进行深度学习优化，成功提高了诊断准确率。