About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Exploring New Frontiers of Thermal Transport: A Combined First-principles and Machine Learning Approach |
Author(s) |
Rinkle Juneja |
On-Site Speaker (Planned) |
Rinkle Juneja |
Abstract Scope |
The behavior of collective atomic vibrations, i.e., phonons, is crucial for understanding stability and thermal transport properties of materials. Given the computational challenges in thermal transport estimation of complex materials, in this talk I will introduce how data-assisted machine learning (ML) can be used for accelerated prediction of transport properties. I will demonstrate the power of ML in discovering new material behaviors and in revealing unusual connections among transport properties using physics-aware descriptors and symmetry-based arguments. I will further showcase deeper fundamental symmetry insights dictating selection rules for thermal transport and unique scattering observables, validated by inelastic neutron scattering measurements, thereby providing new avenues in predicting thermal transport behaviors of materials.
R.J. acknowledges support from the U. S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |