Robust recognition and exploratory analysis of crystal structures using machine learning

In den Materialwissenschaften läuten Künstliche-Intelligenz Methoden einen Paradigmenwechsel in Richtung Big-data zentrierter Forschung ein. Datenbanken mit Millionen von Einträgen, sowie hochauflösende Experimente, z.B. Elektronenmikroskopie, enthalten eine Fülle wachsender Information. Um diese ungenützten, wertvollen Daten für die Entdeckung verborgener Muster und Physik zu nutzen, müssen automatische analytische Methoden entwickelt werden. Die Kristallstruktur-Klassifizierung ist essentiell für die Charakterisierung eines Materials. Vorhandene Daten bieten vielfältige atomare Strukturen, enthalten jedoch oft Defekte und sind unvollständig. Eine geeignete Methode sollte diesbezüglich robust sein und gleichzeitig viele Systeme klassifizieren können, was für verfügbare Methoden nicht zutrifft. In dieser Arbeit entwickeln wir ARISE, eine Methode, die auf Bayesian deep learning basiert und mehr als 100 Strukturklassen robust und ohne festzulegende Schwellwerte klassifiziert. Die einfach erweiterbare Strukturauswahl ist breit gefächert und umfasst nicht nur Bulk-, sondern auch zwei- und ein-dimensionale Systeme. Für die lokale Untersuchung von großen, polykristallinen Systemen, führen wir die strided pattern matching Methode ein. Obwohl nur auf perfekte Strukturen trainiert, kann ARISE stark gestörte mono- und polykristalline Systeme synthetischen als auch experimentellen Ursprungs charakterisieren. Das Model basiert auf Bayesian deep learning und ist somit probabilistisch, was die systematische Berechnung von Unsicherheiten erlaubt, welche mit der Kristallordnung von metallischen Nanopartikeln in Elektronentomographie-Experimenten korrelieren. Die Anwendung von unüberwachtem Lernen auf interne Darstellungen des neuronalen Netzes enthüllt Korngrenzen und nicht ersichtliche Regionen, die über interpretierbare geometrische Eigenschaften verknüpft sind. Diese Arbeit ermöglicht die Analyse atomarer Strukturen mit starken Rauschquellen auf bisher nicht mögliche Weise.In materials science, artificial-intelligence tools are driving a paradigm shift towards big data-centric research. Large computational databases with millions of entries and high-resolution experiments such as electron microscopy contain large and growing amount of information. To leverage this under-utilized - yet very valuable - data, automatic analytical methods need to be developed. The classification of the crystal structure of a material is essential for its characterization. The available data is structurally diverse but often defective and incomplete. A suitable method should therefore be robust with respect to sources of inaccuracy, while being able to treat multiple systems. Available methods do not fulfill both criteria at the same time. In this work, we introduce ARISE, a Bayesian-deep-learning based framework that can treat more than 100 structural classes in robust fashion, without any predefined threshold. The selection of structural classes, which can be easily extended on demand, encompasses a wide range of materials, in particular, not only bulk but also two- and one-dimensional systems. For the local study of large, polycrystalline samples, we extend ARISE by introducing so-called strided pattern matching. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates which are found to be correlated with crystalline order of metallic nanoparticles in electron-tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data

Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy

Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials. Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision. Here, we present AI-STEM, an automatic, artificial-intelligence based method, for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy (STEM) images of polycrystalline materials. The method is based on a Bayesian convolutional neural network (BNN) that is trained only on simulated images. AI-STEM automatically and accurately identifies crystal structure, lattice orientation, and location of interface regions in synthetic and experimental images. The model is trained on cubic and hexagonal crystal structures, yielding classifications and uncertainty estimates, while no explicit information on structural patterns at the interfaces is included during training. This work combines principles from probabilistic modeling, deep learning, and information theory, enabling automatic analysis of experimental, atomic-resolution images.Comment: Code (https://github.com/AndreasLeitherer/ai4stem) and data (https://doi.org/10.5281/zenodo.7756516) are available for public use. The manuscript contains 32 pages (10 pages main text, 15 pages for Methods & References & 5 Figures & 1 Table, as well as 7 pages Supplementary Information), including 5 main figures and 6 supplementary figure

Short-lived star-forming giant clumps in cosmological simulations of z~2 disks

Many observed massive star-forming z\approx2 galaxies are large disks that exhibit irregular morphologies, with \sim1kpc, \sim10^(8-10)Msun clumps. We present the largest sample to date of high-resolution cosmological SPH simulations that zoom-in on the formation of individual M*\sim10^(10.5)Msun galaxies in \sim10^(12)Msun halos at z\approx2. Our code includes strong stellar feedback parameterized as momentum-driven galactic winds. This model reproduces many characteristic features of this observed class of galaxies, such as their clumpy morphologies, smooth and monotonic velocity gradients, high gas fractions (f_g\sim50%) and high specific star-formation rates (\gtrsim1Gyr^(-1)). In accord with recent models, giant clumps (Mclump\sim(5x10^8-10^9)Msun) form in-situ via gravitational instabilities. However, the galactic winds are critical for their subsequent evolution. The giant clumps we obtain are short-lived and are disrupted by wind-driven mass loss. They do not virialise or migrate to the galaxy centers as suggested in recent work neglecting strong winds. By phenomenologically implementing the winds that are observed from high-redshift galaxies and in particular from individual clumps, our simulations reproduce well new observational constraints on clump kinematics and clump ages. In particular, the observation that older clumps appear closer to their galaxy centers is reproduced in our simulations, as a result of inside-out formation of the disks rather than inward clump migration.Comment: 11 pages, 6 figures, 1 table. Accepted for publication in the Astrophysical Journa

Outside-in Shrinking of the Star-forming Disk of Dwarf Irregular Galaxies

We have studied multi-band surface brightness profiles of a representative sample of 34 nearby dwarf irregular galaxies (dIrrs). Our data include GALEX FUV/NUV, UBV, H\alpha, and Spitzer 3.6 \mum images. These galaxies constitute the majority of the LITTLE THINGS survey. By modeling the azimuthal averages of the spectral energy distributions with a complete library of star formation (SF) histories, we derived the stellar mass surface density distributions and the SF rate averaged over three different timescales: the recent 0.1 Gyr, 1 Gyr and a Hubble time. We find that, for \sim 80% (27 galaxies) of our sample galaxies, radial profiles (at least in the outer part) at shorter wavelengths have shorter disk scale lengths than those at longer wavelengths. This indicates that the star-forming disk has been shrinking. In addition, the radial distributions of the stellar mass surface density are well described as piece-wise exponential profiles, and \sim 80% of the galaxies have steeper mass profiles in the outer disk than in the inner region. The steep radial decline of SF rate in the outer parts compared to that in the inner disks gives a natural explanation for the down-bending stellar mass surface density profiles. Within the inner disks, our sample galaxies on average have constant ratios of recent SF rate to stellar mass with radius. Nevertheless, \sim 35% (12 galaxies, among which 7 have baryonic mass < 10^8 M\odot) of the sample exhibit negative slopes across the observed disk, which is in contrast with the "inside-out" disk growth scenario suggested for luminous spiral galaxies. The tendency of SF to become concentrated toward the inner disks in low mass dIrrs is interpreted as a result of their susceptibility to environmental effects and regulation through stellar feedback.Comment: 40 pages. The Astronomical Journal, in pres

The Mice at play in the CALIFA survey: A case study of a gas-rich major merger between first passage and coalescence

We present optical integral field spectroscopy (IFS) observations of the Mice, a major merger between two massive (>10^11Msol) gas-rich spirals NGC4676A and B, observed between first passage and final coalescence. The spectra provide stellar and gas kinematics, ionised gas properties and stellar population diagnostics, over the full optical extent of both galaxies. The Mice provide a perfect case study highlighting the importance of IFS data for improving our understanding of local galaxies. The impact of first passage on the kinematics of the stars and gas has been significant, with strong bars likely induced in both galaxies. The barred spiral NGC4676B exhibits a strong twist in both its stellar and ionised gas disk. On the other hand, the impact of the merger on the stellar populations has been minimal thus far: star formation induced by the recent close passage has not contributed significantly to the global star formation rate or stellar mass of the galaxies. Both galaxies show bicones of high ionisation gas extending along their minor axes. In NGC4676A the high gas velocity dispersion and Seyfert-like line ratios at large scaleheight indicate a powerful outflow. Fast shocks extend to ~6.6kpc above the disk plane. The measured ram pressure and mass outflow rate (~8-20Msol/yr) are similar to superwinds from local ULIRGs, although NGC4676A has only a moderate infrared luminosity of 3x10^10Lsol. Energy beyond that provided by the mechanical energy of the starburst appears to be required to drive the outflow. We compare the observations to mock kinematic and stellar population maps from a merger simulation. The models show little enhancement in star formation during and following first passage, in agreement with the observations. We highlight areas where IFS data could help further constrain the models.Comment: 23 pages, 13 figures, accepted to A&A. A version with a complete set of high resolution figures is available here: http://www-star.st-and.ac.uk/~vw8/resources/mice_v8_astroph.pd

Gaia-ESO Survey: INTRIGOSS - A New Library of High-resolution Synthetic Spectra

We present a high resolution synthetic spectral library, INTRIGOSS, designed for studying FGK stars. The library is based on atmosphere models computed with specified individual element abundances via ATLAS12 code. Normalized SPectra (NSP) and surface Flux SPectra (FSP), in the 4830-5400 A, wavelength range, were computed with the SPECTRUM code. INTRIGOSS uses the solar composition by Grevesse et al. 2007 and four [alpha/Fe] abundance ratios and consists of 15,232 spectra. The synthetic spectra are computed with astrophysical gf-values derived by comparing synthetic predictions with a very high SNR solar spectrum and the UVES-U580 spectra of five cool giants. The validity of the NSPs is assessed by using the UVES-U580 spectra of 2212 stars observed in the framework of the Gaia-ESO Survey and characterized by homogeneous and accurate atmospheric parameter values and by detailed chemical compositions. The greater accuracy of NSPs with respect to spectra from the AMBRE, GES_Grid, PHOENIX, C14, and B17 synthetic spectral libraries is demonstrated by evaluating the consistency of the predictions of the different libraries for the UVES-U580 sample stars. The validity of the FSPs is checked by comparing their prediction with both observed spectral energy distribution and spectral indices. The comparison of FSPs with SEDs derived from ELODIE, INDO--U.S., and MILES libraries indicates that the former reproduce the observed flux distributions within a few percent and without any systematic trend. The good agreement between observational and synthetic Lick/SDSS indices shows that the predicted blanketing of FSPs well reproduces the observed one, thus confirming the reliability of INTRIGOSS FSPs

An uncertainty principle for star formation - II. A new method for characterising the cloud-scale physics of star formation and feedback across cosmic history

The cloud-scale physics of star formation and feedback represent the main uncertainty in galaxy formation studies. Progress is hampered by the limited empirical constraints outside the restricted environment of the Local Group. In particular, the poorly-quantified time evolution of the molecular cloud lifecycle, star formation, and feedback obstructs robust predictions on the scales smaller than the disc scale height that are resolved in modern galaxy formation simulations. We present a new statistical method to derive the evolutionary timeline of molecular clouds and star-forming regions. By quantifying the excess or deficit of the gas-to-stellar flux ratio around peaks of gas or star formation tracer emission, we directly measure the relative rarity of these peaks, which allows us to derive their lifetimes. We present a step-by-step, quantitative description of the method and demonstrate its practical application. The method's accuracy is tested in nearly 300 experiments using simulated galaxy maps, showing that it is capable of constraining the molecular cloud lifetime and feedback time-scale to $<0.1$ dex precision. Access to the evolutionary timeline provides a variety of additional physical quantities, such as the cloud-scale star formation efficiency, the feedback outflow velocity, the mass loading factor, and the feedback energy or momentum coupling efficiencies to the ambient medium. We show that the results are robust for a wide variety of gas and star formation tracers, spatial resolutions, galaxy inclinations, and galaxy sizes. Finally, we demonstrate that our method can be applied out to high redshift ($z\lesssim4$) with a feasible time investment on current large-scale observatories. This is a major shift from previous studies that constrained the physics of star formation and feedback in the immediate vicinity of the Sun

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.Comment: Published in Nature Communications. This document contains main text (4 main figures, 2 main tables) and supplementary information (supplementary methods and notes, as well as 13 supplementary figures and 8 supplementary tables

Testing He ii Emission from Wolf–Rayet Stars as a Dust Attenuation Measure in Eight Nearby Star-forming Galaxies

The ability to determine galaxy properties such as masses, ages, and star formation rates robustly is critically limited by the ability to measure dust attenuation accurately. Dust reddening is often characterized by comparing observations to models of either nebular recombination lines or the UV continuum. Here, we use a new technique to measure dust reddening by exploiting the He ii λ 1640 and λ 4686 emission lines originating from the stellar winds of Wolf–Rayet stars. The intrinsic line ratio is determined by atomic physics, enabling an estimate of the stellar reddening similar to how the Balmer lines probe gas-emission reddening. The He ii line ratio is measured from UV and optical spectroscopy using the Space Telescope Imaging Spectrograph on board the Hubble Space Telescope for eight nearby galaxies hosting young massive star clusters. We compare our results to dust reddening values estimated from UV spectral slopes and from Balmer line ratios and find tentative evidence for systematic differences. The reddening derived from the He ii lines tends to be higher, whereas that from the UV continuum tends to be lower. A larger sample size is needed to confirm this trend. If confirmed, this may indicate an age sequence probing different stages of dust clearing. Broad He ii lines have also been detected in galaxies more distant than in our sample, providing the opportunity to estimate the dust reddening of the youngest stellar populations out to distances of ∼100 Mpc

Effect of Structure and Disorder on the Charge Transport in Defined Self-Assembled Monolayers of Organic Semiconductors

Self-assembled monolayer field-effect transistors (SAMFETs) are not only a promising type of organic electronic device but also allow detailed analyses of structure–property correlations. The influence of the morphology on the charge transport is particularly pronounced, due to the confined monolayer of 2D-π-stacked organic semiconductor molecules. The morphology, in turn, is governed by relatively weak van-der-Waals interactions and is thus prone to dynamic structural fluctuations. Accordingly, combining electronic and physical characterization and time-averaged X-ray analyses with the dynamic information available at atomic resolution from simulations allows us to characterize self-assembled monolayer (SAM) based devices in great detail. For this purpose, we have constructed transistors based on SAMs of two molecules that consist of the organic p-type semiconductor benzothieno­[3,2-<i>b</i>]­[1]­benzothiophene (BTBT), linked to a C₁₁ or C₁₂ alkylphosphonic acid. Both molecules form ordered SAMs; however, our experiments show that the size of the crystalline domains and the charge-transport properties vary considerably in the two systems. These findings were confirmed by molecular dynamics (MD) simulations and semiempirical molecular-orbital electronic-structure calculations, performed on snapshots from the MD simulations at different times, revealing, in atomistic detail, how the charge transport in organic semiconductors is influenced and limited by dynamic disorder