Artificial Intelligence Applied to Photon and Neutron Science

Session

Morning 2

13 Nov 2019, 11:00

70. Data-driven Materials Discovery for Functional Applications

Prof. Jacqui Cole (University of Cambridge/ISIS)

13/11/2019, 11:00

This talk will showcase the use of artificial intelligence (natural language processing, optical character recognition, and machine learning) to auto-generate materials databases for application to areas of interest for neutron science (and the wider materials science community). Specifically, software tools that auto-extract and autonomously analyse materials characterization data will be...

71. Applications of Artificial Neural Networks in Electron Microscopy

Dr Christoph Koch (HU Berlin)

13/11/2019, 11:25

Being charged particles, electrons have a more than 4 orders of magnitude stronger interaction with matter than X-rays or neutrons and may be focused into a spot with less than half an Angstrom in diameter. This makes electron microscopes (EM) very versatile tools for high-resolution imaging, but also diffraction and spectroscopy from very small volumes. The strong interaction with matter,...

72. Deep learning for classifying and sorting diffraction images

Dr Julian Zimmermann (Max-Born-Institut für Nichtlineare Optik und Kurzzeitspektroskopie)

13/11/2019, 11:50

Intense pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nanoparticles in free-flight with a single short-wavelength laser shot. The size of the data sets necessary for successful structure determination, often up to several million diffraction patterns, represents a significant problem for data analysis. Usually, hand-made...

73. PyFitit: the software for quantitative analysis of XANES spectra using machine-learning algorithms

Dr Alexander Guda (The Smart Materials Research Institute, Southern Federal University)

13/11/2019, 12:15

X-ray absorption near-edge spectroscopy (XANES) is becoming an extremely popular tool for material science thanks to the development of new synchrotron radiation light sources. It provides information about charge state and local geometry around atoms of interest in operando and extreme conditions. However, in contrast to X-ray diffraction, a quantitative analysis of XANES spectra is rarely...

85. Machine learning accelerated analysis of materials data: The Smart facility

Prof. Keith Butler (SCD RAL)

14/11/2019, 11:00

If data is oil, then national facilities are vast rich fields - producing terabytes per day. However the great majority of this data is ultimately lost completely. In this talk I will look at how machine learning can allow us to exploit more of this data and extract information. I will present some of the methods that the SciML team at Rutherford Appleton Laboratory is using to accelerate the...

103. Machine Learning for Spectroscopy: From Spectra to Atomic Structures

Dr Marius Retegan (ESRF)

14/11/2019, 11:25

The vast majority of the scientific fields are currently experiencing the machine learning tsunami, as researchers try to exploit their unparallel ability to learn from data to give new insights and make fast predictions of target properties.

Spectroscopy using synchrotron radiation has also seen in recent years an increasing number of applications where machine learning techniques have been...