03/01/2022 - 13:00 - 12:00
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2022-01-03 12:00:00
2022-01-03 13:00:00
קולוקוויום מחלקתי 03.01.2022
הקולוקויום יתקיים ביום שני 03/01/2022 בשעה 12:00בזום
קישור לזום:
https://us02web.zoom.us/j/6155611589
The Department of Chemistry Weekly Seminar will take place online as a Zoom Meeting on Monday 03/01/22, 12:00pm.
For entering the meeting, please click here
Experimental Data-driven Paradigms for Unfolding Complexity in Chemical Systems
Dr. Yevgeny Rakita
(Data Science Institute with Applied Physics and Applied Mathematics, Columbia University, New York, USA)
In complex chemical systems, finding a complete crystallographic model that folds all the interatomic correlations using a small set of structural descriptors may not always be feasible or practical. Alternatively, one can take a data-driven approach and measure the relative changes in structural and/or chemical features (e.g., structural correlations, oxidation states). An experimental data-driven approach does not require complete models and enjoys the rapidly evolving machine-learning tool-set, which excel at classifying relational datasets and, if also labeled by an observed property, can provide predictive power that links system’s descriptors with observed properties. I will focus on two types of complexities: hierarchical complexity, in which different types of structural or chemical correlations change with the probed correlation length, and evolutionary complexity, where the order changes over space and/or time. I will demonstrate how both hierarchical and evolutionary complexity can studied and controlled using a datadriven approach. By looking on Ni-laminated Bulk Metallic Glass as a use case [1] of a complex system, I will show how by treating the data as relational, different aspects of the structural and chemical order, such as chemical-short-range-order, can be directly visualized as a function of position. In a different example [2] I demonstrate an autonomous navigation setup in a complex chemical potential space that will help us to achieve a desired chemical state. In this example, we demonstrate an active reaction control of Cu redox state from real-time feedback from in-situ synchrotron measurements. While complexity can lead to a lack of control over a chemical system, it is essentially adding tuning knobs that, once isolated, understood and controlled, can unlock new materials with desired functionalities.
[1] Y. Rakita, et al., Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM), arXiv:2110.03589 (2021).
[2] Y. Rakita, et al., Active reaction control of Cu redox state based on real-time feedback from in situ synchrotron measurements, JACS 142, 18758 (2020). DOI: 10.1021/jacs.0c09418
Invitation (PDF)
zoom
Department of Chemistry
chemistry.office@biu.ac.il
Asia/Jerusalem
public
הקולוקויום יתקיים ביום שני 03/01/2022 בשעה 12:00בזום
קישור לזום:
https://us02web.zoom.us/j/6155611589
The Department of Chemistry Weekly Seminar will take place online as a Zoom Meeting on Monday 03/01/22, 12:00pm.
For entering the meeting, please click here
Experimental Data-driven Paradigms for Unfolding Complexity in Chemical Systems
Dr. Yevgeny Rakita
(Data Science Institute with Applied Physics and Applied Mathematics, Columbia University, New York, USA)
In complex chemical systems, finding a complete crystallographic model that folds all the interatomic correlations using a small set of structural descriptors may not always be feasible or practical. Alternatively, one can take a data-driven approach and measure the relative changes in structural and/or chemical features (e.g., structural correlations, oxidation states). An experimental data-driven approach does not require complete models and enjoys the rapidly evolving machine-learning tool-set, which excel at classifying relational datasets and, if also labeled by an observed property, can provide predictive power that links system’s descriptors with observed properties. I will focus on two types of complexities: hierarchical complexity, in which different types of structural or chemical correlations change with the probed correlation length, and evolutionary complexity, where the order changes over space and/or time. I will demonstrate how both hierarchical and evolutionary complexity can studied and controlled using a datadriven approach. By looking on Ni-laminated Bulk Metallic Glass as a use case [1] of a complex system, I will show how by treating the data as relational, different aspects of the structural and chemical order, such as chemical-short-range-order, can be directly visualized as a function of position. In a different example [2] I demonstrate an autonomous navigation setup in a complex chemical potential space that will help us to achieve a desired chemical state. In this example, we demonstrate an active reaction control of Cu redox state from real-time feedback from in-situ synchrotron measurements. While complexity can lead to a lack of control over a chemical system, it is essentially adding tuning knobs that, once isolated, understood and controlled, can unlock new materials with desired functionalities.
[1] Y. Rakita, et al., Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM), arXiv:2110.03589 (2021).
[2] Y. Rakita, et al., Active reaction control of Cu redox state based on real-time feedback from in situ synchrotron measurements, JACS 142, 18758 (2020). DOI: 10.1021/jacs.0c09418
Invitation (PDF)
