Related papers: Learning Molecular Chirality via Chiral Determinan…

Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive…

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different…

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…

Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule…

The development of quantitative methods for characterizing molecular chirality can provide an important tool for studying chirality induced phenomena in molecular systems. Significant progress has been made in recent years toward…

Resonance energy transfer between chiral molecules can be used to discriminate between different enantiomers. The transfer rate between chiral molecules consists of a non-discriminatory and discriminatory parts. We derive these two rate…

Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the…

Conventional linear optical activity effects are widely used for studying chiral materials. However, poor contrast and artifacts due to sample anisotropy limit the applicability of these methods. Here we demonstrate that nonlinear…

In photoelectron circular dichroism (PECD) it is generally difficult to trace how and when the chirality of the molecule is imprinted onto the photoelectron. We present simulations of PECD in a simple model and employ chirality measures to…

Causal representation learning (CRL) has garnered increasing interest from the causal inference and artificial intelligence communities due to its potential to disentangle complex data-generating mechanism into causally interpretable latent…

The presence of chirality in the main molecules of life may well be not just a structural artifact, but of pure biological advantage. The possibility of the existence of a phenomenon of a special mode of interaction, labeled as "chiral…

Chirality is ubiquitous from microscopic to macroscopic phenomena in physics and biology, such as fermionic interactions and DNA duplication. In photonics, chirality has traditionally represented differentiated optical responses for right…

Conventional representation learning methods learn a universal representation that primarily captures dominant semantics, which may not always align with customized downstream tasks. For instance, in animal habitat analysis, researchers…

Kernels are often developed and used as implicit mapping functions that show impressive predictive power due to their high-dimensional feature space representations. In this study, we gradually construct a series of simple feature maps that…

We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings. Our approach is based on signature features which can be seen as an ordered variant of sample…

Temporal causal representation learning is a powerful tool for uncovering complex patterns in observational studies, which are often represented as low-dimensional time series. However, in many real-world applications, data are…

Synthetic chiral light enables ultrafast and highly efficient imaging of molecular chirality. Unlike standard circularly polarized light, the handedness of synthetic chiral light does not rely on the spatial structure of the light field: it…

We present a unified description of several methods of chiral discrimination based exclusively on electric-dipole interactions. It includes photoelectron circular dichroism (PECD), enantio-sensitive microwave spectroscopy (EMWS),…

Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance. Such processes can often be accurately modeled via stochastic…

We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures…