Structure in quantum entanglement entropy is often leveraged to focus on a small corner of the exponentially large Hilbert space and efficiently parametrize the problem of finding ground states. A typical example is the use of matrix product states for local and gapped Hamiltonians. We study the structure of entanglement entropy using persistent homology, a relatively new method from the field of topological data analysis. The inverse quantum mutual information between pairs of sites is used as a distance metric to form a filtered simplicial complex. Both ground states and excited states of common spin models are analyzed as an example. Furthermore, the effect of homology with different coefficients and boundary conditions is also explored. Beyond these basic examples, we also discuss the promising future applications of this modern computational approach, including its connection to the question of how space-time could emerge from entanglement.

Indoor radon concentrations are controlled by both human factors and geological factors. It is important to separate the anthropogenic and geogenic contributions. We show that there is a positive correlation between the radiometric map of uranium in the ground and the measured radon in the household in Sweden. A map of gamma radiation is used to obtain an equivalent uranium concentration (ppm eU) for each postcode area. The aggregated uranium content is compared to the yearly average indoor radon concentration for different types of houses. Interestingly, modern households show reduced radon concentrations even in postcode areas with high average uranium concentrations. This shows that modern construction is effective at reducing the correlation with background uranium concentrations and minimizing the health risk associated with radon exposure. These correlations and predictive housing parameters could assist in monitoring higher risk areas.

Context. High-quality vibrational spectra of solid-phase molecules in ice mixtures and for temperatures of astrophysical relevance are needed to interpret infrared observations toward protostars and background stars. Such data are collected worldwide by several laboratory groups in support of existing and upcoming astronomical observations. Over the last 25 yr, the Laboratory for Astrophysics at Leiden Observatory has provided more than 1100 (high-resolution) spectra of diverse ice samples.

Aims. In time with the recent launch of the James Webb Space Telescope, we have fully upgraded the Leiden Ice Database for Astrochemistry (LIDA) adding recently measured spectra. The goal of this paper is to describe what options exist regarding accessing and working with a large collection of infrared (IR) spectra, and the ultraviolet-visible (UV/vis) to the mid-infrared refractive index of H₂O ice. This also includes astronomy-oriented online tools to support the interpretation of IR ice observations.

Methods. This ice database is based on open-source Python software, such as Flask and Bokeh, used to generate the web pages and graph visualization, respectively. Structured Query Language (SQL) is used for searching ice analogs within the database and Jmol allows for three-dimensional molecule visualization. The database provides the vibrational modes of molecules known and expected to exist as ice in space. These modes are characterized using density functional theory with the ORCA software. The IR data in the database are recorded via transmission spectroscopy of ice films condensed on cryogenic substrates. The real UV/vis refractive indices of H₂O ice are derived from interference fringes created from the simultaneous use of a monochromatic HeNe laser beam and a broadband Xe-arc lamp, whereas the real and imaginary mid-IR values are theoretically calculated. LIDA not only provides information on fundamental ice properties, but it also offers online tools. The first tool, SPECFY, is directly linked to the data in the database to create a synthetic spectrum of ices towards protostars. The second tool allows the uploading of external files and the calculation of mid-infrared refractive index values.

Results. LIDA provides an open-access and user-friendly platform to search, download, and visualize experimental data of astrophysically relevant molecules in the solid phase. It also provides the means to support astronomical observations; in particular, those that will be obtained with the James Webb Space Telescope. As an example, we analysed the Infrared Space Observatory spectrum of the protostar AFGL 989 using the resources available in LIDA and derived the column densities of H₂O, CO and CO₂ ices.

Pump–probe time-resolved imaging is a powerful technique that enables the investigation of dynamical processes. Signal-to-noise and sampling rate restrictions normally require that cycles of an excitation are repeated many times with the final signal reconstructed using a reference. However, this approach imposes restrictions on the types of dynamical processes that can be measured, namely, that they are phase locked to a known external signal (e.g., a driven oscillation or impulse). This rules out many interesting processes such as auto-oscillations and spontaneously forming populations, e.g., condensates. In this work we present a method for time-resolved imaging, based on the Schuster periodogram, that allows for the reconstruction of dynamical processes where the intrinsic frequency is not known. In our case we use time of arrival detection of X-ray photons to reconstruct magnetic dynamics without using a priori information on the dynamical frequency. This proof-of-principle demonstration will allow for the extension of pump–probe time-resolved imaging to the important class of processes where the dynamics are not locked to a known external signal and in its presented formulation can be readily adopted for X-ray imaging and also adapted for wider use.

Methodology adapted from data science sparked the field of materials informatics, and materials databases are at the heart of it. Applying artificial intelligence to these databases will allow the prediction of the properties of complex organic crystals.  

Persistent homology (PH) is a relatively new field in applied mathematics that studies the components and shapes of discrete data. In this paper, we demonstrate that PH can be used as a universal framework to identify phases of classical spins on a lattice. This demonstration includes hidden order such as spin-nematic ordering and spin liquids. By converting a small number of spin configurations to barcodes we obtain a descriptive picture of configuration space. Using dimensionality reduction to reduce the barcode space to color space leads to a visualization of the phase diagram.

Dirac and Weyl point- and line-node semimetals are characterized by a zero band gap with simultaneously vanishing density of states. Given a sufficient interaction strength, such materials can undergo an interaction instability, e.g., into an excitonic insulator phase. Due to generically flatbands, organic crystals represent a promising materials class in this regard. We combine machine learning, density functional theory, and effective models to identify specific example materials. Without taking into account the effect of many-body interactions, we found the organic charge transfer salts [bis(3,4-diiodo-3',4'-ethyleneditio-tetrathiafulvalene), 2,3-dichloro-5,6-dicyanobenzoquinone, acetenitrile] [(EDT-TTF-I-2)(2)](DDQ)center dot(CH3CN) and 2, 2', 5, 5'-tetraselenafulvalene-7, 7, 8, 8-tetracyano-p-quinodimethane (TSeF-TCNQ) and a bis-1,2,3-dithiazolyl radical conductor to exhibit a semimetallic phase in our ab initio calculations. Adding the effect of strong particle-hole interactions for (EDT-TTF-I-2)(2)(DDQ)center dot(CH3CN) and TSeF-TCNQ opens an excitonic gap on the order of 60 and 100 meV, which is in good agreement with previous experiments on these materials.

We study the mass fluctuations in gapped Dirac materials by treating the mass term as both a continuous and discrete random variable. Gapped Dirac materials were proposed to be used as materials for dark-matter sensors. One thus would need to estimate the role of disorder and fluctuations on the interband absorption of dark matter. We find that both continuous and discrete fluctuations across the sample introduce tails (e.g., Dirac-Lifshitz tails) in the density of states and the interband absorption rate. We estimate the strength of the gap filling and discuss implications of these fluctuations on the performance as sensors for dark matter detection. The approach used in this work provides a basic framework to model the disorder by any arbitrary mechanism on the interband absorption of Dirac material sensors.

Machine‐learning models are capable of capturing the structure–property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline material. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state‐of‐the‐art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.

Dark Matter particles are commonly assumed to be weakly interacting massive particles (WIMPs) with a mass in the GeV to TeV range. However, recent interest has shifted toward lighter WIMPs, which are more difficult to probe experimentally. A detection of sub-GeV WIMPs will require the use of small gap materials in sensors. Using recent estimates of the WIMP mass, we identify the relevant target space toward small gap materials (100 to 10 meV). Dirac Materials, a class of small- or zero-gap materials, emerge as natural candidates for sensors for Dark Matter detection. We propose the use of informatics tools to rapidly assay materials band structures to search for small gap semiconductors and semimetals, rather than focusing on a few preselected compounds. As a specific example of the proposed strategy, we use the organic materials database () to identify organic candidates for sensors: the narrow band gap semiconductors BNQ-TTF and DEBTTT with gaps of 40 and 38 meV, and the Dirac-line semimetal (BEDT-TTF)center dot Br which exhibits a tiny gap of approximate to 50 meV when spin-orbit coupling is included. We outline a novel and powerful approach to search for dark matter detection sensor materials by means of a rapid assay of materials using informatics tools.