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Antimony selenide (Sb2Se3) is a promising material for solar energy conversion due to its low toxicity, high stability, and excellent light absorption capabilities. However, Sb2Se3 films produced via physical vapor deposition often exhibit Se-deficient surfaces, which result in a high carrier recombination and poor device performance. The conventional selenization process was used to address selenium loss in Sb2Se3 solar cells with a substrate configuration. However, this traditional selenization method is not suitable for superstrated Sb2Se3 devices with the window layer buried underneath the Sb2Se3 light absorber layer, as it can lead to significant diffusion of the window layer material into Sb2Se3 and damage the device. In this work, we have demonstrated a rapid thermal selenization (RTS) technique that can effectively selenize the Sb2Se3 absorber layer while preventing the S diffusion from the buried CdS window layer into the Sb2Se3 absorber layer. The RTS technique significantly reduces carrier recombination loss and carrier transport resistance and can achieve the highest efficiency of 8.25%. Overall, the RTS method presents a promising approach for enhancing low-dimensional chalcogenide thin films for emerging superstrate chalcogenide solar cell applications. 

Abstract In recent years,T_dtransition metal dichalcogenides have been heavily explored for their type‐II Weyl topology, gate‐tunable superconductivity, and nontrivial edge states in the monolayer limit. Here, the Fermi surface characteristics and fundamental transport properties of similarly structured 2M‐WSe₂bulk single crystals are investigated. The measurements of the angular dependent Shubnikov–de Haas oscillations, with support from first‐principles calculations, reveal multiple three‐ and two‐dimensional Fermi pockets, one of which exhibits a nontrivial Berry's phase. In addition, it is shown that the electronic properties of 2M‐WSe₂are similar to those of orthorhombic MoTe₂and WTe₂, having a single dominant carrier type at high temperatures that evolves into coexisting electron and hole pockets with near compensation at temperatures below 100 K, suggesting the existence of a Lifshitz transition. Altogether, the observations provide evidence towards the topologically nontrivial electronic properties of 2M‐WSe₂and motivate further investigation on the topological properties of 2Mtransition metal dichalcogenides in the atomically thin limit. 

We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods. 

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). 

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). 

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). 

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants: iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).