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Meeting MS&T21: Materials Science & Technology
Symposium Phonon Properties of Materials: Modeling and Experimentation
Presentation Title Transfer Learning for Phonon and Thermal Property Predictions
Author(s) Zeyu Liu, Tengfei Luo
On-Site Speaker (Planned) Tengfei Luo
Abstract Scope Machine learning is trending to be an integral part of thermal science. In this talk, I will introduce our efforts in utilizing machine learning (ML) techniques to predict phonon properties and thermal modeling. I will talk about the use of a new ML method, called transfer learning, to establish accurate models based on limited data for predicting phonon and thermal transport properties, such as phonon frequency gap, heat capacity, speed of sound and lattice thermal conductivity.
Proceedings Inclusion? Undecided


Experimental and Computational Thermal Conductivity Reduction in Single Crystal Thorium Dioxide from Lattice Defects
High-temperature Heat Transport in Anharmonic Systems at the Nanoscale
Phonons and Twisting Symmetries in Non-symmorphic Materials
Tailoring Thermal Transport in Insulators Using Energetic Ions
Transfer Learning for Phonon and Thermal Property Predictions
Understanding Ionic Conduction Mechanisms in Glassy Electrolytes Using MD Vibrational Analysis

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