Nature produces a variety of materials with many functions, often out of simple and abundant materials, and at low energy. Such systems - examples of which include silk, bone, nacre or diatoms - provide broad inspiration for engineering. Here we explore the translation of biological composites to engineering applications, using a variety of tools including molecular modeling, AI and machine...
Using subgroup discovery (SGD), an AI approach that discovers statistically exceptional subgroups in a dataset, we develop a strategy for a rational design of catalytic materials. SGD allows for the identification of distinct, possibly competing mechanisms of a catalytic activation. Here, it is applied to the problem of converting CO$_2$ into useful chemicals. We demonstrate that the bending...
Invar alloys exhibit a very low thermal expansion coefficient (TEC) below 2×10−6 K−1 around room temperature. There is a strong impetus to design novel Invar alloys with better physical, mechanical and chemical properties. Here, we develop and apply an active learning strategy to accelerate the design of novel Invar alloys in a practically infinite compositional space of quaternary and quinary...
Passive drug–membrane permeability of a drug molecule quantifies its capacity to cross cell membranes on the
way of reaching its target. In this contribution, I will present results from our work where we used sure-independence screening and sparsifying operator (SISSO) to find equations for the permeability coefficient that combine both hydrophobicity and acidity of the drugs. The predicted...
To extract transferable insights from scanning transmission electron microscopy (STEM), one must deal with noise arising from electron scattering and of the investigated sample. This noise hinders a quantitative analysis of the observation, notably when the features of interest lie in the gradients of the raw data. Physics-informed neural networks have been proposed as a means to incorporate...
Since few years, in addition to classical (simulation-based) HPC usage, we observe a steadily growing need of our users for support of high-performance data-analytics (HPDA) and AI workflows.
In this presentation, we will give an overview of the HPC clusters available at MPCDF for HPDA and AI workflows, the available (HPC-optimized) software stack and we will present recently introduced...
Quantitatively understanding the link between anharmonicity and thermal conductivity, $\kappa$, is pivotal to the search for better thermal insulators. To help find this link we present new descriptors of $\kappa$ based on our new measure of anharmonicity, $\sigma^\mathrm{A}$. Using an updated sure-independence screening and sparsifying operator (SISSO) method, we find analytical expressions...
Normalizing flows are a popular class of models for approximating probability distributions. However, in many tasks such as image generation benchmarks they are still outperformed by autoregressive models and generative adversarial networks. This is in part due to their invertible nature limiting their ability to model target distributions with a complex topological structure. Several...
Microscopy data is often large and 3d, and thus convolutional neural networks (CNNs) need to be applied in a tile-and-stitch manner to cope with GPU memory constraints. Concerning pixel-wise predictions obtained with UNet-style CNNs via tile-and-stitch, issues with discontinuities at output tile boundaries have been reported. However, a formal analysis of the causes has been lacking. In...
Recent developments in bio-imaging technologies have allowed researchers to collect larger and larger tomographic datasets which contain an immense amount of details. To achieve a quantitative understanding, however, these datasets need to be cleaned-up and segmented. These two tasks are tedious, very time consuming, and still performed mostly manually. In our work we aim to develop a full...
L12-type nano-ordered structures are typically fully-coherent with FCC matrix, which is challengeable to be characterized. Spatial distribution maps are used to probe local order within reconstructed APT data. However, it is almost impossible to manually analyse the complete point cloud in search for the partial crystallographic information retained within the data. Here, we proposed an...
Characterizing crystallographic interfaces in synthetic nanomaterials is an important step for the design of novel materials. Trained materials scientists can assign interface structures of materials by looking at high-resolution imaging and diffraction data obtained by aberration-corrected scanning transmission electron microscopy (STEM). However, STEM datasets cannot be fully exploited due...
Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual...
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...
In this poster, we present our research goals of a recently BiGmax funded project towards learning dynamics of scanning transmission electron microscopy (STEM) by incorporating physical consistency with phase-field models. The primary idea of this project is to develop machine learning (ML)-based modeling of an interpretable coarse-grained dynamic model utilizing in situ STEM video sequences...
To correlate mechanical properties of Al alloys with chemical segregation in Atom Probe Tomography (APT), we have developed two approaches. In the first, we collect composition statistics from APT datasets for 2x2x2 nm voxels. These voxel compositions are then clustered in compositional space using Gaussian mixture models to automatically identify key phases and their corresponding statistical...
Mapping of the electronic band structures of materials using momentum microscopy requires processing single-electron events of a few to hundreds of gigabytes. We construct a flexible computational workflow that allows user interaction with billion-count single-electron events in these band mapping experiments. We demonstrate its compatibility with large facility and tabletop experimental...
Data sciences are now also entering theoretical catalysis and energy related research with full might. Automatized workflows and the training of machine learning approaches with first-principles data generate predictive-quality insight into elementary processes and process energetics at undreamed-of pace. Computational screening and data mining allows to explore these data bases for promising...
As an integral part of the FAIR-DI/FAIRmat initiatives, NOMAD is extending it's scope. NOMAD evolves from a central repository for publishing electronic structure codes data into a federated data management network that covers all branches of materials science. Instead of just using NOMAD to publish final results, we want to show how on-site installations of NOMAD can help to manage your local...
Transmission electron microscopy data is rich in quantitative information about materials, information that could in theory be coupled to atomistic simulations, but extracting and harnessing that information is non-trivial. Machine learning approaches may facilitate this, but these are hampered by the limited availability and interoperability of the data. In this talk we present approaches,...
