Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Hyperspectral Image Unmixing Incorporating Adjacency Information

While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials’ spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results

FIAS Scientific Report 2011

In the year 2010 the Frankfurt Institute for Advanced Studies has successfully continued to follow its agenda to pursue theoretical research in the natural sciences. As stipulated in its charter, FIAS closely collaborates with extramural research institutions, like the Max Planck Institute for Brain Research in Frankfurt and the GSI Helmholtz Center for Heavy Ion Research, Darmstadt and with research groups at the science departments of Goethe University. The institute also engages in the training of young researchers and the education of doctoral students. This Annual Report documents how these goals have been pursued in the year 2010. Notable events in the scientific life of the Institute will be presented, e.g., teaching activities in the framework of the Frankfurt International Graduate School for Science (FIGSS), colloquium schedules, conferences organized by FIAS, and a full bibliography of publications by authors affiliated with FIAS. The main part of the Report consists of short one-page summaries describing the scientific progress reached in individual research projects in the year 2010..

Artificial Intelligence based Approach for Rapid Material Discovery: From Chemical Synthesis to Quantum Materials

With the advent of machine learning (ML) in the field of Materials Science, it has become obvious that trained models are limited by the amount and quality of the data used for training. Where researchers do not have access to the breadth and depth of labeled data that fields like image processing and natural language processing enjoy. In the specific application of materials discovery, there is the issue of continuity in atomistic datasets. Often if one relies on experimental data mined from literature and patents this data is only available for the most favorable of atomistic data. This ultimately leads to bias in the training dataset. In providing a solution, this research focuses on investigating the deployment of ML models trained on synthetic data and the development of a language-based approach for synthetically generating training datasets. It has been applied to three material science-related problems to prove these approaches work. The first problem was the prediction of dielectric properties, the second problem was the synthetic generation of chemical reaction datasets, and the third problem was the synthetic generation of quantum material datasets. All three applications proved successful and demonstrated the ability to generate continuous datasets that resolve the issue of dataset bias. This first study investigated the synthetic generation of complex dielectric properties of granular powders and their ability to train a ML network. The neural network was trained using a supervised learning approach and a common backpropagation. The network was double-validated using experimental data collected from a coaxial airline experiment. The second study demonstrated the synthetic generation of a chemical reaction database. An artificial intelligence model based on a Variational Autoencoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions that were assembled into the synthetic dataset. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set. The third study investigated a similar variational autoencoder approach to the second study but with the application of generating a synthetic dataset for quantum materials focusing on quantum sensing applications. The specific quantum sensors of interest are two-level quantum molecules that exhibit dipole blockade. This study offers an improved sampling algorithm by continuously feeding newly generated materials into a sampling algorithm to help generate a more normally distributed dataset. This technique was able to generate over 1,000,000 new quantum materials from a small dataset of only 8,000 materials. From the generated dataset it was identified that several iodine-containing molecules are candidate quantum sensor materials for future studies

Détection de changement par fusion d'images de télédétection de résolutions et modalités différentes

La détection de changements dans une scène est l’un des problèmes les plus complexes en télédétection. Il s’agit de détecter des modifications survenues dans une zone géographique donnée par comparaison d’images de cette zone acquises à différents instants. La comparaison est facilitée lorsque les images sont issues du même type de capteur c’est-à-dire correspondent à la même modalité (le plus souvent optique multi-bandes) et possèdent des résolutions spatiales et spectrales identiques. Les techniques de détection de changements non supervisées sont, pour la plupart, conçues spécifiquement pour ce scénario. Il est, dans ce cas, possible de comparer directement les images en calculant la différence de pixels homologues, c’est-à-dire correspondant au même emplacement au sol. Cependant, dans certains cas spécifiques tels que les situations d’urgence, les missions ponctuelles, la défense et la sécurité, il peut s’avérer nécessaire d’exploiter des images de modalités et de résolutions différentes. Cette hétérogénéité dans les images traitées introduit des problèmes supplémentaires pour la mise en œuvre de la détection de changements. Ces problèmes ne sont pas traités par la plupart des méthodes de l’état de l’art. Lorsque la modalité est identique mais les résolutions différentes, il est possible de se ramener au scénario favorable en appliquant des prétraitements tels que des opérations de rééchantillonnage destinées à atteindre les mêmes résolutions spatiales et spectrales. Néanmoins, ces prétraitements peuvent conduire à une perte d’informations pertinentes pour la détection de changements. En particulier, ils sont appliqués indépendamment sur les deux images et donc ne tiennent pas compte des relations fortes existant entre les deux images. L’objectif de cette thèse est de développer des méthodes de détection de changements qui exploitent au mieux l’information contenue dans une paire d’images observées, sans condition sur leur modalité et leurs résolutions spatiale et spectrale. Les restrictions classiquement imposées dans l’état de l’art sont levées grâce à une approche utilisant la fusion des deux images observées. La première stratégie proposée s’applique au cas d’images de modalités identiques mais de résolutions différentes. Elle se décompose en trois étapes. La première étape consiste à fusionner les deux images observées ce qui conduit à une image de la scène à haute résolution portant l’information des changements éventuels. La deuxième étape réalise la prédiction de deux images non observées possédant des résolutions identiques à celles des images observées par dégradation spatiale et spectrale de l’image fusionnée. Enfin, la troisième étape consiste en une détection de changements classique entre images observées et prédites de mêmes résolutions. Une deuxième stratégie modélise les images observées comme des versions dégradées de deux images non observées caractérisées par des résolutions spectrales et spatiales identiques et élevées. Elle met en œuvre une étape de fusion robuste qui exploite un a priori de parcimonie des changements observés. Enfin, le principe de la fusion est étendu à des images de modalités différentes. Dans ce cas où les pixels ne sont pas directement comparables, car correspondant à des grandeurs physiques différentes, la comparaison est réalisée dans un domaine transformé. Les deux images sont représentées par des combinaisons linéaires parcimonieuses des éléments de deux dictionnaires couplés, appris à partir des données. La détection de changements est réalisée à partir de l’estimation d’un code couplé sous condition de parcimonie spatiale de la différence des codes estimés pour chaque image. L’expérimentation de ces différentes méthodes, conduite sur des changements simulés de manière réaliste ou sur des changements réels, démontre les avantages des méthodes développées et plus généralement de l’apport de la fusion pour la détection de changement

Advances of Machine Learning in Materials Science: Ideas and Techniques

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.Comment: 80 pages; 22 figures. To be published in Frontiers of Physics, 18, xxxxx, (2023

Probing the cosmic-ray pressure in the Virgo Cluster and the origin of the very-high-energy gamma rays of M87 with H.E.S.S. and CTA

Das High Energy Stereoscopic System (H.E.S.S.) ist ein System von fünf atmosphärischen Cherenkov-Teleskopen (IACT) in Namibia. Die H.E.S.S. Teleskope sind empfindlich für sehr energiereiche (VHE) Gammastrahlen zwischen ~30 TeV und 100 TeV. Mit einer Entfernung von 16,5 Mpc ist Messier 87 (M87) eine der nächsten Radiogalaxien und beherbergt eines der massereichsten supermassiven Schwarzen Löcher, das Materie in einen Plasmastrahl relativistischer Teilchen emittiert. Der Strahl wird im Bereich des gesamten elektromagnetischen Spektrums beobachtet und untersucht. M87 befindet sich im Zentrum des Virgo-Galaxienhaufens, eines kühlen Galaxienhaufens, der von Gas gefüllt ist, das in der Nähe des Zentrums kälter und in den Außenbereichen des Galaxienhaufens heißer ist. Gemäß der Cooling Flow (CF) Theorie kühlt das Plasma in Cool Core (CC) Haufen am Rand des Haufens ab und sinkt nach innen, wodurch die Sternentstehungsrate im Zentrum erhöht wird. Optische Messungen des Virgo Galaxienhaufens scheinen diesem Modell jedoch zu widersprechen. Als Heizmechanismus wird der aktive galaktische Kern Rückkopplungsmechanismus vorgeschlagen, der die Abkühlung des ICM ausgleicht und dessen CF vermeidet. Die kosmische Strahlung des Jets interagiert mit der ICM und erzeugt neutrale Pionen, die in Gammastrahlen zerfallen und ein nicht variables und ausgedehntes Gammastrahlensignal erzeugen. Allerdings konnten keine Gammastrahlen-Beobachtungen mit dem Pionenzerfall in dem Galaxienhaufen in Verbindung gebracht werden. In dieser Studie der H.E.S.S. Beobachtungen des niedrigen Strahl-Aktivitätszustands von M87 haben keine signifikante Ausdehnung der Emissionsregion gezeigt, woraus eine 3σ Obergrenze von 0.016° ≈ 4.6 kpc abgeleitet wurde. Das Verhältnis des Drucks in kosmischer Strahlung zur thermischen Strahlung ist auf <0.36 im Zentralregion beschränkt. Diese abgeleitete Obergrenze nimmt einen Gleichgewichtszustand zwischen den Erwärmungs und den Kühlprozessen an. Die neue Generation von IACTs, das Cherenkov Telescope Array Observatory (CTAO), wird eine unvergleichbare Empfindlichkeit und Winkelauflösung bieten. Um die langfristige Verfügbarkeit der Teleskope sicherzustellen, wurde ein auf Schwingungsmessungen basierendes Strukturüberwachungssystem entwickelt und zwischen 2019 und 2020 in Berlin am Prototyp des mittelgrossen Teleskopes erfolgreich getestet. CTAO wird in der Lage sein die Gammastrahlung des Virgo Haufens zu untersuchen und sie laut Simulationen und dem Steady-State-Modell innerhalb von ~210 h zu detektieren.The High Energy Stereoscopic System (H.E.S.S.) is an array of five Imaging Atmospheric Cherenkov Telescopes (IACTs) located in Namibia. The H.E.S.S. telescopes are sensitive to Very-High-Energy (VHE) gamma rays between ~30 TeV and ~100 TeV. At a distance of 16.5 Mpc Messier 87 (M87) is one of the closest radio-galaxies, hosting one of the most massive Super-Massive Black Hole, which accretes matter and launches an inclined jet of relativistic particles. The jet is detected and studied by radiation emitted through the entire electromagnetic spectrum. M87 is located at the very center of the Virgo galaxy cluster, a Cool Core (CC) cluster, characterized by an Intra-cluster Medium (ICM) that is colder close to the center and hotter towards the outskirts of the galaxy cluster. According to the Cooling Flow (CF) theory, the plasma in CC clusters cools in the outskirts of the cluster and falls inwards, increasing the star formation ratio in the region. However, optical measurements of the Virgo Cluster seem to contradict this model. The Active Galactic Nucleus (AGN) feedback mechanism is proposed as a heating mechanism, which counterbalances the cooling of the ICM and avoids its CF. The cosmic rays from the jet interact with the ICM producing neutral pions, which decay to gamma rays, forming a non-variable and extended gamma-ray signal. However, no gamma-ray observations could be associated with pion decay in galaxy clusters. In this work, deep H.E.S.S. observations of M87's low state are analyzed, and the results have shown no significant gamma-ray extension leading to a 3σ upper limit of 0.016° ≈ 4.6 kpc. The ratio of cosmic-ray pressure to thermal pressure XCR is constrained to < 0.36 at its maximum position, assuming a steady-state between the heating and the cooling processes. The new generation of IACTs, the Cherenkov Telescope Array Observatory (CTAO) will offer unprecedented sensitivity and angular resolution. To assure the long-term availability of the telescopes, a structure monitoring system based on vibration measurements was developed and successfully tested at the Medium-sized Telescope (MST) prototype between 2019 and 2020 in Berlin. CTAO should be able to probe the gamma-ray emission from the Virgo Cluster, and, according to simulations and to the steady-state model, significantly detect it after ≈ 210 h