Tutorial EN07—Machine Learning Toward Advanced Thermal Materials

Monday, April 22, 2024

1:30 pm – 5:00 pm

Summit - Seattle Convention Center, Level 3, Room 344

Instructor: Ming Hu, University of South Carolina

The advent of machine learning (ML) and artificial intelligence (AI) has revolutionized many aspects of modern science and technology and has sparked significant interest in the materials science community in recent years. Despite some early deployment of ML/AI in the thermal science area, the power of AI has not been maximized. Existing ML methods for predicting phonon properties of crystals are limited to either a small amount of training data or a material-to-material basis, primarily due to the exponential scaling of model parameters with the number of atomic species or elements. This renders high-throughput infeasible when facing large-scale new materials.

This tutorial lecture will introduce some state-of-the-art ML/AI approaches for predicting energy carriers' transport behaviors in materials. Both traditional ML methods (such as random forest) and novel graph neural networks will be presented with showcase studies on training, testing and prediction of lattice vibrations (phonons). Particular focus will be our recently developed Elemental Spatial Density Neural Network Force Field (Elemental-SDNNFF) with abundant atomic-level environments as training data. Benefiting from the innovative architecture of the algorithm, sub-trillion atomic data can be integrated to train a single deep neural network for predicting complete phonon properties of >100,000 inorganic crystals spanning 63 elements in the periodic table.

We will also illustrate recent ML/AI algorithms for discovering promising thermal materials for various energy applications, including but not limited to thermoelectrics, interfacial thermal management, topological phonons for quantum information technology.

Tutorial Schedule

1:30 pm

Machine Learning Toward Advanced Thermal Materials—Algorithm

Ming Hu, University of South Carolina

2:45 pm BREAK

3:15 pm

Machine Learning Toward Advanced Thermal Materials—Applications

Ming Hu, University of South Carolina