A Physics-Informed Evolutionary Transfer Optimization Framework for Material Design

The design of new crystal materials is of significant scientific importance to society. In recent years, machine learning-based approaches have shown their potential in crystal material design. However, their effectiveness relies heavily on the availability of high-quality and extensive training data, which is difficult to collect in practice. To this end, this paper presents a novel physics-informed evolutionary transfer optimization framework that can design new crystal materials without the need for extensive data. Specifically, we first propose a novel physics-informed encoding for materials, enabling the use of multi-objective evolutionary optimization to simultaneously optimize multiple physical objectives, including the validity, properties, and energy of crystal materials. These physical objectives are critical to the effective design of crystal materials. Additionally, to mitigate the slow optimization speed of evolutionary computation, we propose a physics-informed evolutionary transfer optimization technique to enhance the design speed of optimized materials. We conducted comprehensive experiments to analyze the designed crystals from the perspectives of validity, density functional theory (DFT) validation, formation energy, and energy above hull. The experimental results validate the immense potential of the proposed physics-informed multi-objective evolutionary optimization framework in crystal material design.