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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. 

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). 

Antimony selenide (Sb₂Se₃) emerges as a promising sunlight absorber in thin film photovoltaic applications due to its excellent light absorption properties and carrier transport behavior, attributed to the quasi‐one‐dimensional Sb₄Se₆‐nanoribbon crystal structure. Overcoming the challenge of aligning Sb₂Se₃‐nanoribbons normal to substrates for efficient photogenerated carrier extraction, a solution‐processed nanocrystalline Sb₂(S,Se)₃‐seeds are employed on the CdS buffer layer. These seeds facilitate superstrated Sb₂Se₃thin film solar cell growth through a close‐space sublimation approach. The Sb₂(S,Se)₃‐seeds guided the Sb₂Se₃absorber growth along a [002]‐preferred crystal orientation, ensuring a smoother interface with the CdS window layer. Remarkably, Sb₂(S,Se)₃‐seeds improve carrier transport, reduce series resistance, and increase charge recombination resistance, resulting in an enhanced power conversion efficiency of 7.52%. This cost‐effective solution‐processed seeds planting approach holds promise for advancing chalcogenide‐based thin film solar cells in large‐scale manufacturing. 

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at \url{https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench}. 

Thanks to the rapid advances in artificial intelligence, AI for science (AI4Science) has emerged as one of the new promising research directions for modern science and engineering. In this review, we focus on recent efforts to develop knowledge-driven Bayesian learning and experimental design methods for accelerating the discovery of novel functional materials as well as enhancing the understanding of composition-process-structure-property relationships. We specifically discuss the challenges and opportunities in integrating prior scientific knowledge and physics principles with AI and machine learning (ML) models for accelerating materials and knowledge discovery. The current state-of-the-art methods in knowledge-based prior construction, model fusion, uncertainty quantification, optimal experimental design, and symbolic regression are detailed in the review, along with several detailed case studies and results in materials discovery.