A FAIR research data management is of fundamental importance for new discoveries in materials science and related disciplines. This is even more so in case "FAIR" is interpreted as "Findable, and AI Ready". In order to facilitate the process to a FAIR data management across all scientific disciplines and to leverage the hidden treasures in available experimental and computational data sets,...
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials-science data. As a major improvement, we introduce a crystal-structure identification method based on Bayesian deep learning that is robust to structural noise and can treat more than 100 crystal structures. While being trained on ideal structures only, our method...
Predicting the emergent properties of a material from a microscopic description is a scientific challenge. Machine learning and reverse-engineering have opened new paradigms in the understanding and design of materials. However, the soft-matter field has lagged far behind in embracing this approach for materials design. The main difficulty stems from the importance of entropy, the ubiquity of...
Segmentation and analysis of structures in 3d biological samples may be an ambiguous operation, due to the difficulties in the data visualization. Machine learning may help for this kind of task, but they may remain opaque regarding the scientific reasons leading to a particular result. Thanks to recent advancement in the field of explainable machine learning, human interpretable explanations...
We developed a machine-learning-based approach for solving computing the elastoplastic mechanical response of polycrystalline structures. In particular, a recursive deep neural network based on U-Net and applied recursively is proposed as a surrogate model for predicting the von Mises stress field under quasi-static tensile loading. We show that the model can accurately predict both the...
The performance of heterogeneous catalysts is governed by an intricate interplay of several multi-scale processes. Thus, it is rather challenging to identify the most relevant parameters for the design of the catalyst and its support material. Here, we combine experimental and theoretical descriptive parameters characterizing cobalt nanoparticles dispersed on SiO2 supports modified...
An accurate description of the surface of Pd-based catalysts under reaction conditions is a critical step toward a deeper understanding of catalyst reactivity. Herein, by modeling the phase diagram of the (111) and (100) surfaces of face-centered cubic Pd via ab initio atomistic thermodynamics, we predict the stable hydrogen coverages for a wide range of temperatures and H2 pressures. The...
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) obtains chemical information on a sub-micron scale. Traditionally, experts analyze the spectra in a time-consuming manner, and the complexity of the data limits what can be extracted by inspection. Machine learning could push the limits of ToF-SIMS on various aspects. Machine-learning-enhanced identification of atomic and molecular...
Photo-electron spectra obtained with intense pulses generated by free-electron lasers through self-amplified spontaneous emission are intrinsically noisy and vary from shot to shot. We extract the purified spectrum, corresponding to a Fourier-limited pulse, with the help of a deep neural network. It is trained on a huge number of spectra, which was made possible by an extremely efficient...
Many materials science studies use scanning transmission electron microscopy (STEM) to characterize atomic-scale structure. Conventional STEM imaging experiments produce only a few intensity values at each probe position. However, modern high-speed detectors allow us to measure a full 2D diffraction pattern, over a grid of 2D probe positions, forming a four dimensional (4D)-STEM dataset. These...
We present the development of an open source tool within the Python library pyxem for automated crystal orientation mapping in the scanning transmission electron microscope (STEM). An efficient and flexible template matching algorithm is developed, where simulated electron diffraction patterns are compared to experimental patterns obtained from scanning precession nanobeam electron...
In the Visual Computing and Artificial Intelligence Department at MPI for Informatics, we investigate research questions at the intersection of computer graphics, computer vision and artificial intelligence. In this presentation, I will talk about some of the recent work we did on new methods for reconstructing high quality computer graphics models (shape, motion, appearance, material,...
In this work the concepts from scientific machine learning are employed to learn continuum phase field models directly from the experimental data of Scanning Transmission Electron Microscopy (STEM). Currently, we assume the form of the continuum model is known to be as Cahn-Hilliard/Allen-Cahn equations with a prior expression for free energy function. The unknown parameters of the continuum...
Atom probe tomography is now an established near atomic-scale characterization technique. However, the traditional analysis often limits the subtle inherent details of field evaporation processes occurring near defects or multiple phases. We present two cases employing unconventional data mining routines on experimental data to extract valuable physical insights, supported by simulations....
Atom probe tomography (APT) is a unique technique that provides 3D elemental distribution with near atomic resolution for a given material. However, the large amount of data acquired during the experiment and the complexity of the 3D microstructures poses a challenge to fully quantify APT data. Here, taking APT measurements corresponding to a Fe-doped Sm-Co alloy as an example, we present an...
Chemical short-range order (CSRO), referring to specific elements self-organising within a disordered matrix, can modify the properties of materials. CSRO is typically characterized via two-dimensional microscopy techniques that fail to capture three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to reveal three-dimensional imaging of CSRO in...
Classical and machine learning interatomic potentials alike incorporate design choices that reflect the intuition of their authors and that are justified only a-posteriori by the performance of the model. Design choices comprise, for example, the form of the embedding function of an embedded atom potential or a specific angular dependence of a descriptor in a machine learning potential.
The...
Autonomous materials discovery with desired properties is one of the ultimate goals of materials science. We implemented and applied constrained crystal deep convolutional generative adversarial networks to design unreported (meta-)stable crystal structures. Using an image-based continuous latent space, the physical properties can be optimized while exploring a big chemical space. Our approach...
Intermetallic clathrate alloys are promising materials for thermoelectric applications. Their cage-like unit cell allows for tailoring the electronic properties through doping. Yet, a realistic theoretical description is hard to achieve due to the complex interplay between temperature, (dis)order and electronic properties. In this work, we show a novel approach to compute the...
Sampling from Boltzmann distributions through normalizing flows promises to be computationally much cheaper than molecular dynamics (MD) simulations. However, flows struggle to approximate complicated target distributions due to topological constraints and still heavily rely on MD samples to be trained on. Here, we present two lines of research addressing these issues, the former by...
