Search Results for author:
Found 11 papers, 0 papers with code
Self-Supervised Learning in Electron Microscopy: Towards a Foundation Model for Advanced Image Analysis
In this work, we explore the potential of self-supervised learning from unlabeled electron microscopy datasets, taking a step toward building a foundation model in this field.
Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
Detecting and analyzing various defect types in semiconductor materials is an important prerequisite for understanding the underlying mechanisms as well as tailoring the production processes.
DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials
Crystalline materials, such as metals and semiconductors, nearly always contain a special defect type called dislocation.
Machine learning for structure-guided materials and process design
The second is to solve a process design problem that is to find an optimal processing path to manufacture these material structures.
A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables
In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures.
Modeling Dislocation Dynamics Data Using Semantic Web Technologies
Research in the field of Materials Science and Engineering focuses on the design, synthesis, properties, and performance of materials.
Unsupervised Learning of Nanoindentation Data to Infer Microstructural Details of Complex Materials
In this study, Cu-Cr composites were studied by nanoindentation.
Instance Segmentation of Dislocations in TEM Images
In the domain of materials science, the knowledge about the location and movement of dislocations is important for creating novel materials with superior properties.
Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties
Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science.
Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data"
The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification, digitization, and quantitative extraction of the dislocations as curved objects.
Automated analysis of continuum fields from atomistic simulations using statistical machine learning
Continuum scale constitutive models for material behavior can benefit from information on the atomic scale, in particular in terms of the deformation mechanisms, the accommodation of the total strain and partitioning of stress and strain fields in individual grains.
