Publications

The interaction of strong laser fields with matter intrinsically provides powerful tools to image transient dynamics with an extremely high spatiotemporal resolution. Here, we study strong-field ionisation of laser-aligned molecules and show a full real-time picture of the photoelectron dynamics in the combined action of the laser field and the molecular interaction. We demonstrate that the molecule has a dramatic impact on the overall strong-field dynamics: it sets the clock for the emission of electrons with a given rescattering kinetic energy. This result represents a benchmark for the seminal statements of molecular-frame strong-field physics and has strong impact on the interpretation of self-diffraction experiments. Furthermore, the resulting encoding of the time-energy relation in molecular-frame photoelectron momentum distributions shows the way of probing the molecular potential in real-time and accessing a deeper understanding of electron transport during strong-field interactions.

We present a beyond-mean-field approach to predict the nature of organic polariton lasing, accounting for all relevant photon modes in a planar microcavity. Starting from a microscopic picture, we show how lasing can switch between polaritonic states resonant with the maximal gain, and those at the bottom of the polariton dispersion. We show how the population of non-lasing modes can be found, and by using two-time correlations, we show how the photoluminescence spectrum (of both lasing and non-lasing modes) evolves with pumping and coupling strength, confirming recent experimental work on the origin of blueshift for polariton lasing.

Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector. In this paper we derive formulae for general permutation equivariant layers, including the case where the layer acts on matrices by permuting their rows and columns simultaneously. This case arises naturally in graph learning and relation learning applications. As a specific case of higher order permutation equivariant networks, we present a second order graph variational encoder, and show that the latent distribution of equivariant generative models must be exchangeable. We demonstrate the efficacy of this architecture on the tasks of link prediction in citation graphs and molecular graph generation.

Recently, the twist angle between adjacent sheets of stacked van der Waals materials emerged as a new knob to engineer correlated states of matter in two-dimensional heterostructures in a controlled manner, giving rise to emergent phenomena such as superconductivity or correlated insulating states. Here,we use an ab initio based approach to characterize the electronic properties of twisted bilayer MoS2. We report that, in marked contrast to twisted bilayer graphene, slightly hole-doped MoS2 realizes a strongly asymmetric px-py Hubbard model on the honeycomb lattice, with two almost entirely dispersionless bands emerging due to destructive interference. We study the collective behavior of twisted bilayer MoS2 in the presence of interactions, and characterize an array of different magnetic and orbitally-ordered correlated phases,which may be susceptible to quantum fluctuations giving rise to exotic, purely quantum, states of matter.

Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

Hermann Bondi's 1952 paper "On spherically symmetrical accretion" is recognized as one of the foundations of accretion theory. Although Bondi later remarked that it was "not much more than an examination exercise", his mathematical analysis of spherical accretion on to a point mass has found broad use across fields of astrophysics that were embryonic or non-existent at the time of the paper's publication. In this non-technical review, I describe the motivations for Bondi's work, and briefly discuss some of the applications of Bondi accretion in high energy astrophysics, galaxy formation, and star formation.

Dirac fermions with highly dispersive linear bands are usually considered weakly correlated due to the relatively large bandwidths (W) compared to Coulomb interactions (U). With the discovery of nodal-line semimetals, the notion of the Dirac point has been extended to lines and loops in momentum space. The anisotropy associated with nodal-line structure gives rise to greatly reduced kinetic energy along the line. However, experimental evidence for the anticipated enhanced correlations in nodal-line semimetals is sparse. Here, we report on prominent correlation effects in a nodal-line semimetal compound, ZrSiSe, through a combination of optical spectroscopy and density functional theory calculations. We observed two fundamental spectroscopic hallmarks of electronic correlations: strong reduction (1/3) of the free-carrier Drude weight and also the Fermi velocity compared to predictions of density functional band theory. The renormalization of Fermi velocity can be further controlled with an external magnetic field. ZrSiSe therefore offers the rare opportunity to investigate correlation-driven physics in a Dirac system.

Over the last few years, extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high degree of precision. An appealing and challenging route toward engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, i.e., to provide a unique framework that allows us to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory. This article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, such as the new theoretical framework of quantum electrodynamics density-functional formalism for the description of novel light–matter hybrid states. Those advances, and others being released soon as part of the Octopus package, will allow the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (quantum electrodynamical-materials).

In the course of doing astronomy, one often encounters plots of densities, for example probability densities, flux densities, and mass functions. Quite frequently the ordinate of these diagrams is plotted logarithmically to accommodate a large dynamic range. In this situation, I argue that it is critical to adjust the density appropriately, rather than simply setting the x-scale to `log' in your favorite plotting code. I will demonstrate the basic issue with a pedagogical example, then mention a few common plots where this may arise, and finally some possible exceptions to the rule.