Generative deep learning for predicting ultrahigh lattice thermal conductivity materials

Abstract

Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion. The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. However, these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties, as they lack physical constraints and information about the complexity of atomic many-body interactions. Here, we show how combining deep generative models of crystal structures with quantum-accurate, fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality. We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model. Additionally, we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials. Applying this method to carbon materials, we screen 100,000 candidates and identify 34 carbon polymorphs, approximately a quarter of which had not been previously predicted, to have lattice thermal conductivity above 800 W m⁻¹ K⁻¹, reaching up to 2,400 W m⁻¹ K⁻¹ aside from diamond. These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.