LSM - Seminars: April 2022 Archives

Seminars Archives

March 2022 | April 2022 | September 2022

Thursday, April 07, 2022
Global Optimization of Atomic Structures with Machine Learning Karsten W. Jacobsen Department of Physics Technical University of Denmark 12:00pm - ZOOM Artificial intelligence is paving the way for new approaches to the understanding and design of materials at the atomic and electronic level. Machine learning can provide guidance for high-throughput computational searches and in some situations effectively replace computer intensive electronic structure calculations. In the talk I shall focus on one particular application of machine learning: the global optimization of atomic structures. The methodology is based on Gaussian processes and uses the calculated model uncertainties to guide the search [1]. Furthermore, the machine learning approach allows for an extension of the configuration space to consider atoms, which are not pure elements, but combinations of several elements (for example an atom which is a combination of gold and copper). This circumvents barriers in the potential energy surface and makes structure determination much more efficient. The approach is illustrated with applications to clusters, surfaces, and bulk systems. [1] S. Kaappa, C. Larsen, and K. W. Jacobsen, “Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements,” Phys. Rev. Lett., vol. 127, no. 16, p. 166001, Oct. 2021, doi: 10.1103/PhysRevLett.127.166001. Join Zoom Meeting at: https://rutgers.zoom.us/j/94435281331?pwd=cXRXeWhySFJOb3ZnZGFnSFdRQkR2QT09 Meeting ID: 944 3528 1331 Password: 091967

Thursday, April 07, 2022

Global Optimization of Atomic Structures with Machine Learning
Karsten W. Jacobsen
Department of Physics
Technical University of Denmark
12:00pm - ZOOM

Artificial intelligence is paving the way for new approaches to the understanding and design of materials at the atomic and electronic level. Machine learning can provide guidance for high-throughput computational searches and in some situations effectively replace computer intensive electronic structure calculations.

In the talk I shall focus on one particular application of machine learning: the global optimization of atomic structures. The methodology is based on Gaussian processes and uses the calculated model uncertainties to guide the search [1]. Furthermore, the machine learning approach allows for an extension of the configuration space to consider atoms, which are not pure elements, but combinations of several elements (for example an atom which is a combination of gold and copper). This circumvents barriers in the potential energy surface and makes structure determination much more efficient. The approach is illustrated with applications to clusters, surfaces, and bulk systems.

[1] S. Kaappa, C. Larsen, and K. W. Jacobsen, “Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements,” Phys. Rev. Lett., vol. 127, no. 16, p. 166001, Oct. 2021, doi: 10.1103/PhysRevLett.127.166001.

Join Zoom Meeting at:

https://rutgers.zoom.us/j/94435281331?pwd=cXRXeWhySFJOb3ZnZGFnSFdRQkR2QT09

Meeting ID: 944 3528 1331

Password: 091967

Thursday, April 14, 2022
Surface Modification for Solar Cells and Solar Fuels: from Dye Sensitization to Halide Perovskite Yiying Wu Chemistry Department The Ohio State University 12:00pm - ZOOM Semiconductor-electrolyte interfaces play a crucial role in controlling the interfacial electron transfer for solar cells and solar fuels. In the first part of my talk, I will talk about the materials design of molecular sensitizers for nanostructured NiO to achieve the control of directional charge transfer between photo-excited sensitizers and NiO. It provides an attractive platform for p-type dye-sensitized solar cells and photocatalytic reactions including water reduction for hydrogen generation and oxygen generation for H2O2 production. With the emergence of halide perovskite solar cells, many groups working on dye-sensitization faced the struggle of transitions, including my group. In the second part of my talk, I will describe our recent progresses in porphyrin-perovskite solar cells and machine-learning-assisted model for predicting structures of low-dimensional halide perovskites. Join Zoom Meeting at: https://rutgers.zoom.us/j/94435281331?pwd=cXRXeWhySFJOb3ZnZGFnSFdRQkR2QT09 Meeting ID: 944 3528 1331 Password: 091967

Thursday, April 14, 2022

Surface Modification for Solar Cells and Solar Fuels: from Dye Sensitization to Halide Perovskite
Yiying Wu
Chemistry Department
The Ohio State University
12:00pm - ZOOM

Semiconductor-electrolyte interfaces play a crucial role in controlling the interfacial electron transfer for solar cells and solar fuels. In the first part of my talk, I will talk about the materials design of molecular sensitizers for nanostructured NiO to achieve the control of directional charge transfer between photo-excited sensitizers and NiO. It provides an attractive platform for p-type dye-sensitized solar cells and photocatalytic reactions including water reduction for hydrogen generation and oxygen generation for H2O2 production. With the emergence of halide perovskite solar cells, many groups working on dye-sensitization faced the struggle of transitions, including my group. In the second part of my talk, I will describe our recent progresses in porphyrin-perovskite solar cells and machine-learning-assisted model for predicting structures of low-dimensional halide perovskites.

Join Zoom Meeting at:

https://rutgers.zoom.us/j/94435281331?pwd=cXRXeWhySFJOb3ZnZGFnSFdRQkR2QT09

Meeting ID: 944 3528 1331

Password: 091967

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