Related papers: E(3)-equivariant models cannot learn chirality: Fi…

Fine management of chiral processes on solid surfaces has progressed over the years, yet still faces the need for the controlled and selective production of advanced chiral materials. Here, we report on the use of enantiomerically enriched…

Chirality is a ubiquitous phenomenon in the natural world. Many biomolecules without inversion symmetry such as amino acids and sugars are chiral molecules. Measuring and controlling molecular chirality at a high precision down to the…

The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D object parameters. Such an approach works well when supervision…

Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their…

Implicit neural representations have emerged as a powerful tool in learning 3D geometry, offering unparalleled advantages over conventional representations like mesh-based methods. A common type of INR implicitly encodes a shape's boundary…

Point cloud analysis is an area of increasing interest due to the development of 3D sensors that are able to rapidly measure the depth of scenes accurately. Unfortunately, applying deep learning techniques to perform point cloud analysis is…

Chirality arises from the asymmetry of matters, where two counterparts are the mirror image of each other. The interaction between circular-polarization light and quantum materials is enhanced in chiral space groups due to the structural…

Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties. Previous generative models have focused on producing SMILES strings or 2D molecular…

Convolutional Neural Networks (CNNs) traditionally encode translation equivariance via the convolution operation. Generalization to other transformations has recently received attraction to encode the knowledge of the data geometry in group…

A current challenge in atomistic machine learning is that of efficiently predicting the response of the electron density under electric fields. We address this challenge with symmetry-adapted kernel functions that are specifically derived…

Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics. In this paper, we focus on 3D deformable shapes that share a common topological structure, such as human faces and bodies.…

Detecting and controlling the chirality of materials play an essential role in exploring nature, providing new avenues for material creation, discrimination, and manipulation. In such tasks, chiral reagents are essential in defining or…

Many quantities we are interested in predicting are geometric tensors; we refer to this class of problems as geometric prediction. Attempts to perform geometric prediction in real-world scenarios have been limited to approximating them…

Chirality in polymeric systems enables a wide range of emergent optical, mechanical, and transport phenomena, yet a unified framework that quantitatively connects molecular-scale geometry to chiral behavior remains lacking. Existing…

The neural radiance field (NERF) advocates learning the continuous representation of 3D geometry through a multilayer perceptron (MLP). By integrating this into a generative model, the generative neural radiance field (GRAF) is capable of…

We investigate quantum transport in a two-dimensional electron system coupled to a chiral molecular potential, demonstrating how molecular chirality and orientation affect charge and spin transport properties. We propose a minimal model for…

A symmetry on rigid motion is one of the salient factors in efficient learning of 3D point cloud problems. Group convolution has been a representative method to extract equivariant features, but its realizations have struggled to retain…

While impressive progress has recently been made in image-oriented facial attribute translation, shape-oriented 3D facial attribute translation remains an unsolved issue. This is primarily limited by the lack of 3D generative models and…

3D anomaly detection in point-cloud data is critical for industrial quality control, aiming to identify structural defects with high reliability. However, current memory bank-based methods often suffer from inconsistent feature…

We analyze two simple model planar molecules: an ionic molecule with D3 symmetry and a covalent molecule with D6 symmetry. Both symmetries allow the existence of chiral molecular orbitals and normal modes that are coupled to each other in a…