1. |
- Niemi, MEK, et al.
(författare)
-
- 2021
-
swepub:Mat__t
|
|
2. |
- Kanai, M, et al.
(författare)
-
- 2023
-
swepub:Mat__t
|
|
3. |
|
|
4. |
|
|
5. |
|
|
6. |
|
|
7. |
|
|
8. |
|
|
9. |
- Barillot, T., et al.
(författare)
-
Correlation-Driven Transient Hole Dynamics Resolved in Space and Time in the Isopropanol Molecule
- 2021
-
Ingår i: Physical Review X. - : American Physical Society. - 2160-3308. ; 11:3
-
Tidskriftsartikel (refereegranskat)abstract
- The possibility of suddenly ionized molecules undergoing extremely fast electron hole (or hole) dynamics prior to significant structural change was first recognized more than 20 years ago and termed charge migration. The accurate probing of ultrafast electron hole dynamics requires measurements that have both sufficient temporal resolution and can detect the localization of a specific hole within the molecule. We report an investigation of the dynamics of inner valence hole states in isopropanol where we use an x-ray pump-x-ray probe experiment, with site and state-specific probing of a transient hole state localized near the oxygen atom in the molecule, together with an ab initio theoretical treatment. We record the signature of transient hole dynamics and make the first tentative observation of dynamics driven by frustrated Auger-Meitner transitions. We verify that the effective hole lifetime is consistent with our theoretical prediction. This state-specific measurement paves the way to widespread application for observations of transient hole dynamics localized in space and time in molecules and thus to charge transfer phenomena that are fundamental in chemical and material physics.
|
|
10. |
- Sanchez-Gonzalez, A., et al.
(författare)
-
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
- 2017
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
-
Tidskriftsartikel (refereegranskat)abstract
- Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.
|
|