Study Liquid Structure and Transport Properties Using Molecular Dynamics Simulation and Machine Learning

Type Of Event
Seminar
Location
Microsoft Teams
Speaker
Dr. Haimeng Wang, Material Science Division, Argonne National Laboratory
Host
Arthur Glowacki
Start Date
07-11-2023
Start Time
9:00 a.m.
Description

Abstract:

Molecular dynamics (MD) simulations have emerged as an important tool for investigating the physical properties of materials and gaining microscopic-level insights. To ensure the accuracy of MD simulations, precise calculation of the potential energy surface (PES) is crucial. Several approaches have been employed to accurately depict the PES, including fixed-charge models, polarizable models, ab initio calculations, and machine learning. In this research talk, I will dive into a comprehensive discussion on the advantages and limitations of each approach, examining their fidelity in relation to experimental results such as X-ray scattering and transport property measurements. This talk will encompass specific examples from molten salt and battery electrolyte systems, illustrating the application of these approaches in practical scenarios. By examining these case studies, we aim to elucidate the strengths and weaknesses of different methodologies and facilitate a deeper understanding of the material behavior under investigation.

Location:

Join Teams Meeting

https://teams.microsoft.com/l/meetup-join/19%3ameeting_YzMzNDY2ZTAtYmExYS00ZWQ4LTgxODctZDQ4MTNjMTBhOWQ1%40thread.v2/0?context=%7b%22Tid%22%3a%220cfca185-25f7-49e3-8ae7-704d5326e285%22%2c%22Oid%22%3a%22c1b97440-7149-429c-a337-88f29b15c9b2%22%7d

Host:

Arthur Glowacki

 

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