We consider the problem of generating periodic materials with deep models.
While symmetry-aware molecule generation has been studied extensively, periodic
materials possess different symmetries, which have not been completely captured
by existing methods. In this work, we propose SyMat, a novel material
generation approach that can capture physical symmetries of periodic material
structures. SyMat generates atom types and lattices of materials through
generating atom type sets, lattice lengths and lattice angles with a
variational auto-encoder model. In addition, SyMat employs a score-based
diffusion model to generate atom coordinates of materials, in which a novel
symmetry-aware probabilistic model is used in the coordinate diffusion process.
We show that SyMat is theoretically invariant to all symmetry transformations
on materials and demonstrate that SyMat achieves promising performance on
random generation and property optimization tasks.

我们提出了 SyMat，一种能够捕捉周期性材料结构物理对称性的新型材料生成方法，其通过生成原子类型集，晶格长度和晶格角度的变分自动编码器模型来生成材料的原子类型和晶格，同时，SyMat 采用基于打分的扩散模型来生成材料的原子坐标，在坐标扩散过程中使用了一种新颖的对称感知概率模型。我们证明 SyMat 在材料的所有对称变换下都是理论不变的，并证明 SyMat 在随机生成和性能优化任务上取得了令人满意的表现。

朝向对称感知的周期材料生成

Towards Symmetry-Aware Generation of Periodic Materials

Detecting objects and estimating their 6D poses is essential for automated
systems to interact safely with the environment. Most 6D pose estimators,
however, rely on a single camera frame and suffer from occlusions and
ambiguities due to object symmetries. We overcome this issue by presenting a
novel symmetry-aware multi-view 6D pose estimator called SyMFM6D. Our approach
efficiently fuses the RGB-D frames from multiple perspectives in a deep
multi-directional fusion network and predicts predefined keypoints for all
objects in the scene simultaneously. Based on the keypoints and an instance
semantic segmentation, we efficiently compute the 6D poses by least-squares
fitting. To address the ambiguity issues for symmetric objects, we propose a
novel training procedure for symmetry-aware keypoint detection including a new
objective function. Our SyMFM6D network significantly outperforms the
state-of-the-art in both single-view and multi-view 6D pose estimation. We
furthermore show the effectiveness of our symmetry-aware training procedure and
demonstrate that our approach is robust towards inaccurate camera calibration
and dynamic camera setups.

该论文介绍了一种新的对物体进行 6D 姿态估计的方法，其中使用了多视角深度网络、预定义关键点、实例语义分割等技术，特别考虑了对称物体的问题，相对于现有技术有很大的提升。