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Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems.

 

Computation-guided selection of dopants enables the transformation of Hg₂GeTe₄from intrinsic to degenerate carrier concentrations and the thermoelectric performance is assessed experimentally.

 

Diamond like semiconductors (DLS) have emerged as candidates for thermoelectric energy conversion. Towards understanding and optimizing performance, we present a comprehensive investigation of the electronic properties of two DLS phases, quaternary Cu 2 HgGeTe 4 and related ordered vacancy compound Hg 2 GeTe 4 , including thermodynamic stability, defect chemistry, and transport properties. To establish the thermodynamic link between the related but distinct phases, the stability region for both is visualized in chemical potential space. In spite of their similar structure and bonding, we show that the two materials exhibit reciprocal behaviors for dopability. Cu 2 HgGeTe 4 is degenerately p-type in all environments despite its wide stability region, due to the presence of low-energy acceptor defects V Cu and Cu Hg and is resistant to extrinsic n-type doping. Meanwhile Hg 2 GeTe 4 has a narrow stability region and intrinsic behavior due to the relatively high formation energy of native defects, but presents an opportunity for bi-polar doping. While these two compounds have similar structure, bonding, and chemical constituents, the reciprocal nature of their dopability emerges from significant differences in band edge positions. A Brouwer band diagram approach is utilized to visualize the role of native defects on carrier concentrations, dopability, and transport properties. This study elucidates the doping asymmetry between two solid-solution forming DLS phases Cu 2 HgGeTe 4 and Hg 2 GeTe 4 by revealing the defect chemistry of each compound, and suggests design strategies for defect engineering of DLS phases.