TY - JOUR A1 - Martynec, T. A1 - Karapanagiotis, Christos A1 - Klapp, S. H. L. A1 - Kowarik, S. T1 - Machine learning predictions of surface migration barriers in nucleation and non-equilibrium growth JF - Communications materials N2 - Machine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters. KW - Machine learning KW - Surface migration barriers KW - Material engineering PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-532523 UR - https://www.nature.com/articles/s43246-021-00188-1 DO - https://doi.org/10.1038/s43246-021-00188-1 SN - 2662-4443 VL - 2 SP - 1 EP - 9 PB - Springer Nature CY - London AN - OPUS4-53252 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Wosniok, Aleksander A1 - Hicke, Konstantin A1 - Krebber, Katerina T1 - Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis JF - Sensors N2 - To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential. KW - Fiber-optic sensors KW - Machine learning KW - Temperature and strain monitoring KW - Brillouin distributed sensing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524631 DO - https://doi.org/10.3390/s21082724 VL - 21 IS - 8 SP - 2724 PB - MDPI AN - OPUS4-52463 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Time-efficient CNN-assisted BOFDA N2 - Brillouin Optical frequency domain analysis (BOFDA) is a well-known technique in distributed Fiber sensing for temperature and strain monitoring. In this study, we applied a signal post-processing method based on convolutional neural networks (CNNs) and we show that in comparison to conventional methods based on Lorentzian curve fitting (LCF), our CNN model is more robust against noise and can shorten the measurement time by more than 9 times. This will open the way for applications, where faster monitoring is needed. T2 - Machine learning workshop CY - Online meeting DA - 18.03.2021 KW - Temperature and strain sensing KW - Machine learning KW - Distributed fiber-optic sensors PY - 2021 AN - OPUS4-52305 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -