About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Energy Materials for Sustainable Development
|
| Presentation Title |
Search for New Magnetocaloric Materials Via Computation and Machine Learning |
| Author(s) |
Bolin Liao |
| On-Site Speaker (Planned) |
Bolin Liao |
| Abstract Scope |
Magnetic cooling based on magnetocaloric effect has attracted a lot of interest because of its promise of high efficiency and solid-state operation. In this talk, I will highlight our recent efforts of using first principles atomistic spin dynamics and spin-lattice dynamics simulations to model and predict magnetocaloric performance in emerging materials. I will discuss our work on understanding spin-lattice coupling in Gd and MnAs, as well as the impact of dimensionality on 2D magnets. I will also describe our efforts of using machine learning to search for new magnetocaloric materials, which led to the discovery of EuB6 as a promising material for hydrogen liquefaction, which was recently confirmed experimentally. |