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CsYbSe2 has an ideal triangular-lattice geometry with pronounced two-dimensionality, pseudospin-1/2 nature, and the absence of structural disorder. These excellent characteristics favor a quantum spin-liquid realization in this material. In this work, we applied quasihydrostatic compression methods to explore the structural behaviors. Our study reveals that CsYbSe2 undergoes a structural transition around 24 GPa, accompanied by a large volume collapse of ΔV /V0∼13%. The ambient hexagonal structure with the space group P63/mmcis lowered to the tetragonal structure (P4/mmm) under high pressure. Meanwhile, the color of CsYbSe2 changes gradually from red to black before the transition. Dramatic pressure-induced changes are clarified by the electronic structure calculations from the first principles, which indicate that the initial insulating ground state turns metallic in a squeezed lattice. These findings highlight Yb-based dichalcogenide delafossites as an intriguing material to probe novel quantum effects under high pressure. 

Topology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly complicated problems. However, with hundreds of design variables or more involved, solving such problems would require millions of Finite Element Method (FEM) calculations whose computational cost is huge and impractical. Here we report Self-directed Online Learning Optimization (SOLO) which integrates Deep Neural Network (DNN) with FEM calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN’s prediction of the optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. The optimum predicted by the DNN is proved to converge to the true global optimum through iterations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 ~ 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. This approach enables solving large multi-dimensional optimization problems.