We apply first-principles calculations to lead selenide (PbSe) and lead telluride (PbTe) and their alloys (PbTe 1-x Se x ) which are potentially good thermoelectric materials, to investigate their phonon transport properties. By accurately reproducing the lattice thermal conductivity, we validate the approaches adopted in this work. We then compare and contrast PbSe and PbTe, evaluate the importance of the optical phonons to lattice thermal conductivity, and estimate the impacts of nanostructuring and alloying on further reducing the lattice thermal conductivity. The results indicate that: 1) the optical phonons are important not only because they directly comprise over 20% of the lattice thermal conductivity, but also because they provide strong scattering channels for acoustic phonons, which is crucial for the low thermal conductivity; 2) nanostructures of less than ~10 nm are needed to reduce the lattice thermal conductivity for pure PbSe and PbTe; 3) alloying should be a relatively effective way to reduce the lattice thermal conductivity.
The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure-property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18-0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermoplastics .
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