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Electronic Correlations and Hund’s Rule Coupling in Trilayer Nickelate La4Ni3O10

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arXiv 2024年

作者： Huo, Zihao Zhang, Peng Zhang, Zihan Duan, Defang Cui, Tian Key Laboratory of Material Simulation Methods & Software of Ministry of Education State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China MOE Key Laboratory for Non-equilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Advanced Functional Materials and Mesoscopic Physics School of Physics Xi’an Jiaotong University Xi’an710049 China Institute of High Pressure Physics School of Physical Science and Technology Ningbo University Ningbo315211 China

Trilayer Ruddlesden–Popper phase La4Ni3O10 has been observed with Tc of ~ 30 K at high pressure in recent experiment, which further expanded the family of nickelate superconductors. In this study, we explored the effects of electronic correlations in La4Ni3O10 using density function theory plus dynamical mean–field theory at ambient and high pressures. Our derived spectral functions and Fermi surface of ambient pressure phase are nicely consistent with the experimental results by angle–resolved photoemission spectroscopy, which emphasized the importance of electronic correlations in La4Ni3O10. We also found the electronic correlations in pressured La4Ni3O10 are both orbital–dependent and layer–dependent due to the presence of Hund’s rule coupling. There is a competition between the Hund’s rule coupling and the crystal–field splitting, and therefore the Ni–O layers with weaker crystal-field splitting energy would have stronger electronic correlations. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoacoustic spectroscopy

Deep learning generative model for crystal structure prediction

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arXiv 2024年

作者： Luo, Xiaoshan Wang, Zhenyu Gao, Pengyue Lv, Jian Wang, Yanchao Chen, Changfeng Ma, Yanming Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun130012 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States

Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3,547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

Prediction of high-Tc superconductivity under submegabar pressure in ternary actinium borohydrides

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arXiv 2024年

作者： Gu, Tingting Cui, Wenwen Hao, Jian Shi, Jingming Durajski, Artur P. Liu, Hanyu Li, Yinwei Laboratory of Quantum Functional Materials Design and Application School of Physics and Electronic Engineering Jiangsu Normal University Xuzhou221116 China Institute of Physics Czestochowa University of Technology Ave. Armii Krajowej 19 Czestochowa42-200 Poland Key Laboratory of Material Simulation Methods and Software of Ministry of Education State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China

Ternary hydrides are considered as the idea candidates with high critical temperature (Tc) stabilized at submegabar pressure, evidenced by the recent discoveries in LaBeH8 (110 K at 80 GPa) and LaB2H8 (106 K at 90 GPa). Here, we investigate the crystal structures and superconductivity of Ac-B-H system under pressures of 100 and 200 GPa by using an advanced structure method combined with first-principles calculations. As a result, nine stable compounds were identified, where B atoms are bonded with H atoms in formation with diverse BHx motifs, e.g., methanelike (BH4), polythenelike, (BH2)n, and BH6 octahedron. Among them, seven Ac-B-H compounds were found to become superconductive. In particular, AcBH7 was estimated to have a Tc of 122 K at 70 GPa. Our in-depth analysis reveals that the B-H interactions in the BH6 units play a key role in its high superconductivity and stability at submegar pressure. Our current results provide a guidance for future experiments to synthesize ternary hydride superconductors with high-Tc at moderate pressure. © 2024, CC0.

关键词： Hydrides

CrystalFlow: A Flow-Based Generative Model for Crystalline materials

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arXiv 2024年

作者： Luo, Xiaoshan Wang, Zhenyu Lv, Jian Wang, Lei Wang, Yanchao Ma, Yanming Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun130012 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approach to efficiently explore the configuration space of crystalline materials. In this work, we present CrystalFlow, a flow-based generative model specifically developed for the generation of crystalline materials. CrystalFlow constructs Continuous Normalizing Flows to model lattice parameters, atomic coordinates, and/or atom types, which are trained using Conditional Flow Matching techniques. Through an appropriate choice of data representation and the integration of a graph-based equivariant neural network, the model effectively captures the fundamental symmetries of crystalline materials, which ensures data-efficient learning and enables high-quality sampling. Our experiments demonstrate that CrystalFlow achieves state-of-the-art performance across standard generation benchmarks, and exhibits versatile conditional generation capabilities including producing structures optimized for specific external pressures or desired material properties. These features highlight the model’s potential to address realistic crystal structure prediction challenges, offering a robust and efficient framework for advancing data-driven research in condensed matter physics and material science. Copyright © 2024, The Authors. All rights reserved.

关键词： Crystalline materials

New MgSiO4H2 Phases as Potential Primary Water Carriers into the Deep Earth

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Physical Review Letters 2024年第21期133卷 214101-214101页

作者： Sen Shao Jian Lv Xin Li Lu Li Peng Liu Zhaodong Liu Changfeng Chen Yanchao Wang Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun 130012 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun 130012 China Synergetic Extreme Condition User Facility College of Physics Jilin University Changchun 130012 China College of Earth Sciences Jilin University Changchun 130061 China Department of Physics and Astronomy University of Nevada Las Vegas Nevada 89154 USA International Center of Future Science Jilin University Changchun 130012 China

Dense hydrous magnesium silicate MgSiO4H2 is widely regarded as a primary water carrier into the deep Earth. However, the stability fields of MgSiO4H2 based on the prevailing structure model are narrower than experimental results at relevant pressure and temperature (P-T) conditions, casting doubts about this prominent mineral as a water carrier into the great depths of the Earth. Here, we report on an advanced structure search that identifies two new crystal structures, denoted as α- and β−MgSiO4H2, that are stable over unprecedentedly wide P-T conditions of 17–68 GPa and up to 1860 K, covering the entire experimentally determined range. Moreover, we performed x-ray diffraction measurements with backscattering electron image, combined with ab initio simulations, to demonstrate the formation of MgSiO4H2 and AlOOH solid solutions that exhibit further enhanced P-T stability fields, making them robust carriers of water into the deepest lower mantle. These findings establish and elucidate the new MgSiO4H2 phases as potential primary water carriers into the vast depths of the lower mantle, creating a distinct paradigm for the deep Earth water cycle.

关键词： Crystal structure Earth's interior First-principles calculations Geophysics High-pressure studies

Space Group Informed Transformer for Crystalline materials Generation

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arXiv 2024年

作者： Cao, Zhendong Luo, Xiaoshan Lv, Jian Wang, Lei Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun130012 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China

We introduce CrystalFormer, a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. The incorporation of space group symmetry significantly simplifies the crystal space, which is crucial for data and compute efficient generative modeling of crystalline materials. Leveraging the prominent discrete and sequential nature of the Wyckoff positions, CrystalFormer learns to generate crystals by directly predicting the species and locations of symmetry-inequivalent atoms in the unit cell. We demonstrate the advantages of CrystalFormer in standard tasks such as symmetric structure initialization and element substitution compared to conventional methods implemented in popular crystal structure prediction software. Moreover, we showcase the application of CrystalFormer of property-guided materials design in a plug-and-play manner. Our analysis shows that CrystalFormer ingests sensible solid-state chemistry knowledge and heuristics by compressing the material dataset, thus enabling systematic exploration of crystalline materials. The simplicity, generality, and flexibility of CrystalFormer position it as a promising architecture to be the foundational model of the entire crystalline materials space, heralding a new era in materials modeling and discovery. Copyright © 2024, The Authors. All rights reserved.

关键词： Crystalline materials

Concurrent learning scheme for crystal structure prediction

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Physical Review B 2024年第9期109卷 094117-094117页

作者： Zhenyu Wang Xiaoyang Wang Xiaoshan Luo Pengyue Gao Ying Sun Jian Lv Han Wang Yanchao Wang Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun 130012 People's Republic of China International Center of Future Science Jilin University Changchun 130012 People's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing 100094 People's Republic of China HEDPS CAPT College of Engineering Peking University Beijing 100871 People's Republic of China

Crystal structure prediction (CSP) and machine learning potential (MLP) are two fundamental methods for modern computational material discovery. While the former aims at efficient sampling of the potential energy surface (PES) for discovering new materials, the latter focuses on reproducing the PES to accelerate various atomic simulation tasks. In this work, we combine the two methods within a concurrent learning framework in an effort to generate efficient MLP models for accelerating CSP. The proposed scheme explores the PES through the swarm-intelligence calypso method, labels the most representative structures with quantum mechanical calculations, and learns the PES through a deep potential (DP) model. The process proceeds in an iterative, computationally efficient, and automated manner, leading to the collection of a most compact reference training set from which the resulting DP model is proven particularly suitable for accelerating calypso structure prediction. The scheme has been systematically benchmarked on binary magnesium-aluminium (Mg-Al) alloys and ternary lithium-lanthanum-hydrogen (Li-La-H) superhydrides, demonstrating its efficiency and reliability in DP model construction and calypso structure prediction. The proposed scheme represents a promising routine to perform the structure prediction of large or multicomponent systems.

关键词： Crystal structure Potential energy surfaces Machine learning

FeSiO4H2 stabilized at subducting slab conditions: A geologically viable water carrier into the Earth's lower mantle

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Physical Review B 2023年第21期108卷 214107-214107页

作者： Sen Shao Jian Lv Hanyu Liu Wencheng Lu Peng Liu Andreas Hermann Yanchao Wang Changfeng Chen Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun 130012 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun 130012 China International Center of Future Science Jilin University Changchun 130012 China Centre for Science at Extreme Conditions and Scottish Universities Physics Alliance School of Physics and Astronomy The University of Edinburgh Edinburgh EH9 3FD United Kingdom Department of Physics and Astronomy University of Nevada Las Vegas Nevada 89154 USA

Hydrous minerals hold the key to unlocking the enduring mystery of the water cycle deep inside the Earth. Tremendous efforts have been devoted to identifying geologically viable minerals meeting stringent pressure-temperature-density stability requirements for descent into deep Earth, and such pursuits remain active. Here, we identify two hydrous iron silicates, α- and β−FeSiO4H2, formed by a reaction of Earth-abundant FeSiO3 and H2O and stabilized at the pressure-temperature conditions in cold subducting slabs. These phases have a sufficiently high density for a stable descent into the Earth's lower mantle, and then decompose to release water after reaching equilibrium with the mantle geotherm. Moreover, Mg(Fe)SiO4H2 solutions are found to be more stable than the pure substances and can serve as effective carriers to transport substantial amounts of water to lower-mantle regions via the cold subduction zones. These findings establish a viable and robust material basis for the deep-Earth water cycle, with major implications for elucidation of many prominent geological processes.

关键词： Electrical properties First-principles calculations High-pressure studies Phase diagrams

Electrochemical Failure Mechanism of Δ-Mno2 in Zinc Ion Batteries Induced by Irreversible Layered to Spinel Phase Transition

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SSRN

SSRN 2023年

作者： Zhao, Chunyu Wu, Mengqi Lu, Wencheng Cheng, Yingjie Zhang, Xiaoya Lian, Ruqian Wang, Yizhan Wei, Yingjin College of Physics Jilin University Changchun130012 China Chongqing Research Institute Jilin University Chongqing401123 China Key Laboratory of Optic-Electronic Information and Materials of Hebei Province National-Local Joint Engineering Laboratory of New Energy Photoelectric Devices College of Physics Science and Technology Hebei University Baoding071002 China Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun130012 China

Phase transitions of Mn-based cathode materials associated with the charge and discharge process play a crucial role on the rate capability and cycle life of zinc ion batteries. Herein, a microscopic electrochemical failure mechanism of Zn-MnO2 batteries during the phase transitions from δ-MnO2 to λ-ZnMn2O4 is presented via systematic first-principle investigation. The initial insertion of Zn2+ intensifies the rearrangement of Mn. This is completed by the electrostatic repulsion and co-migration between guest and host ions, leading to the formation of λ-ZnMn2O4. The Mn relocation barrier for the λ-ZnMn2O4 formation path with 1.09 eV is significantly lower than the δ-MnO2 re-formation path with 2.14 eV, indicating the irreversibility of the layered-to-spinel transition. Together with the phase transition, the rearrangement of Mn elevates the Zn2+ migration barrier from 0.31 eV to 2.28 eV, resulting in poor rate performance. With the increase of charge-discharge cycles, irreversible and inactive λ-ZnMn2O4 products accumulate on the electrode, causing continuous capacity decay of the Zn-MnO2 battery. © 2023, The Authors. All rights reserved.

关键词： Zinc compounds