| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Frontiers of Machine Learning on Materials Discovery
|
| Presentation Title |
Inverse Design of Quantum Materials by High-Throughput Calculations and Optimization Techniques |
| Author(s) |
Ying Wai Li, Christopher Lane, Jianxin Zhu |
| On-Site Speaker (Planned) |
Ying Wai Li |
| Abstract Scope |
Quantum materials that exhibit novel magnetism and unconventional superconductivity hold promise for future quantum sensing and quantum computing applications. However, designing novel materials with desired properties is challenging because of the vast geometrical and compositional degrees of freedom in the design space. In this talk, we will highlight our recent efforts in designing novel f-electron quantum materials by combining first principles calculations with machine learning techniques. We first perform high-throughput Density Functional Theory to calculate essential physical properties that affect the Kondo temperature, such as the electronic density of states at Fermi level, bandwidth, f-ligand hybridization, and the Hubbard on-site potential. We then inverse design the materials to enhance the Kondo temperature by performing an iterative, guided search for the best crystal structure and composition using the reinforcement learning-based Monte Carlo Tree Search algorithm. |