18.07.23 - Atreyee Banerjee "Data-driven identification and analysis of the glass transition in polymer melts"
| When |
Jul 18, 2023
from 03:00 PM to 04:00 PM |
|---|---|
| Where | HS II, Physics Highrise |
| Contact Name | Simone Ortolf |
| Contact Phone | 203-97666 |
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Data-driven identification and analysis of the glass transition in polymer melts
On fast cooling, the dynamical properties of many polymer melts slow down exponentially, leading to a glassy state without any drastic change in the static structure. Understanding the nature of glass transition, as well as precise estimation of the glass transition temperature ($T_g$) even for a fixed cooling rate, remain open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information of individual chains. It clearly identifies the glass transition temperature of polymer melts of semiflexible chains.
By combining principal component analysis (PCA) and clustering, we identify glass transition temperature at the asymptotic limit even from relatively short-time trajectories, which just reach into the Rouse-like monomer displacement regime [1]. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behaviour: from conformational rearrangement above to small vibrations below the glass transition temperature. We demonstrate the generality of the approach by using different dimensionality reduction and clustering approaches. The method can be applied to a wide range of systems with microscopic/atomistic information. More recently we applied this methodology to all-atom acrylic paint systems [2].
[1] Data-driven identification and analysis of the glass transition in polymer melts,
Atreyee Banerjee, Hsiao-Ping Hsu, Kurt Kremer, Oleksandra Kukharenko, ACS Macro Lett. 2023, 12, 6, 679–684 (2023)
[2] Determining glass transition temperature in acrylic polymeric melts with machine learning methods, Atreyee Banerjee, Aysenur Iscen, Kurt Kremer, and Oleksandra Kukharenko (under revision J. Chem. Phys.)
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