Machine Learning for Materials Design

Simulation of Magneto-Structural Phase Transitions in Materials

Research

We bridge the divide between atomistic simulations at the microscopic scale and material simulations at the mesoscopic scale. By combining high-fidelity data generation with machine-learning models, we create interatomic potentials for high-performance molecular-spin dynamics simulations.

By combining high-fidelity data generation with machine-learning models, we create interatomic potentials for high-performance molecular-spin dynamics simulations.

This methodology couples lattice degrees of freedom with electronic spins. We utilize this method for tackling a wide range of applications, including the analysis of material strength, the investigation of transport properties in nanoscale systems, and the simulation of magneto-structural phase transitions relevant for developing ultrafast magnetic storage devices.

We have developed a data-driven approach to create magneto-elastic machine learning models to simulate large-scale molecular spin dynamics. These models help us understand magneto-structural phase transitions in materials. We build these models by combining a collective atomic spin model with a machine learning interatomic potential. Both parts of the model are fine-tuned using data from first-principles calculations. We build these models by combining a collective atomic spin model with a machine learning interatomic potential.

Both parts of the model are fine-tuned using data from first-principles calculations. We tested our approach by applying it to alpha-iron, a material that undergoes magneto-structural phase transitions. Our results show how our method accurately predicts several material properties, such as bulk modulus, magnetization and specific heat, as well as the Curie temperature during the transition from a ferromagnetic to a paramagnetic state.