Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning

Abstract

Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond.

References in corpus (6)

• Distribution of phonon lifetime in Brillouin zone
• Data-driven materials science: status, challenges and perspectives
• High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion
• Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
• Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling
• High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites

Cited by in corpus (6)

• RadonPy: Automated Physical Property Calculation using All-atom Classical Molecular Dynamics Simulations for Polymer Informatics
• Machine learning reconstruction of depth-dependent thermal conductivity profile from pump-probe thermoreflectance signals
• High thermoelectric performance in metastable phase of silicon: a first-principles study
• PINK: physical-informed machine learning for lattice thermal conductivity
• Accelerating the Discovery of Materials with Expected Thermal Conductivity via a Synergistic Strategy of DFT and Interpretable Deep Learning
• Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach