上海期智研究院PI，复旦大学物理学系教授，中国科学院院士，计算凝聚态物理学家，计算物质科学教育部重点实验室主任。
主要从事凝聚态体系结构、电子结构和计算方法发展研究。
个人荣誉
1993年中国科学院院长奖学金特别奖
1995年中国科学院青年科学家奖
1995年安徽省优秀出国人员奖
2009年美国物理学会会士
机器学习势函数的开发：通过发展新的模型架构，提高势函数的拟合精度，尤其是对常程相互作用的拟合
电子结构计算方法：实现复杂体系的物性计算和模拟，研究表面、界面、铁电、磁性、电解质等复杂体系的结构和物性
动力学模拟和材料设计方法：对材料的离子和电子含时演化进行动力学模拟，在此基础之上理论设计新型光电、多铁、催化、能量存储等功能材料。
成果9：基于多体矫正项的机器学习势函数新框架的开发
近十年来迅速发展起来的高维机器学习势代表着复杂系统大规模原子模拟的巨大进步。然而高维机器学习势函数普遍具有长程相互作用和化学反应描述不准确的问题，其主要原因是以原子为中心的 机器学习模型结构判别能力差。项目提出了一种基于神经网络的低成本机器学习势函数结构，用于拟合全局势能面数据，即多体矫正机器学习神经网络势函数，它能在复杂势能面上提供更好的结构分辨能力，从而提高对能量和力的拟合精度。在多体矫正神经网络势函数中，计算总能量时明确包含了一组基于局部原子环境的多体能量项，而系统的总能量被写成原子能量和多体能量贡献之和。这些额外的多体能量项计算成本较低，而且重要的是，它们可以方便地获取复杂系统中的微妙能量项，如极短斥力、长程吸引力和敏感的角度依赖性共价相互作用。我们在 LASP 代码中实现了多体矫正机器学习神经网络势函数，并选取三元能量材料 LiCoOx、有缺陷的 TiO2 以及一系列有机反应作为代表性体系展示了新的势函数框架在不同场景中表现出的更高的计算精度。
研究领域：机器学习神经网络势函数
项目网站： http://www.lasphub.com/
研究论文：Pei-Lin Kang, Zheng-Xin Yang, Cheng Shang and Zhi-Pan Liu, Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface, J. Chem. Theory Comput., 2023, 19, 7972-7981. 查看PDF
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成果8：可迁移的大规模电子结构计算的机器学习加速方法
密度泛函理论（DFT）是研究分子和材料电子结构的强大工具，它能够揭示许多物质性质的内在机制。然而，由于DFT在大型系统上计算时所需的高昂计算成本和运行时间，使得在此类系统中成功实施DFT计算仍然受到诸多限制。复旦大学的向红军团队提出了基于图神经网络实现的电子哈密顿矩阵的等变参数化方法，该方法可以实现从原子位置到电子哈密顿量的直接映射，从而绕过DFT方法中昂贵的自洽迭代过程。在碳同素异形体、硅同素异形体和SiO2异构体的哈密顿矩阵上分别进行训练后的HamGNN模型对训练集之外的同类结构预测的能带与DFT计算得到的能带高度一致。此外，训练之后的HamGNN模型还成功预测了含上千原子的大型硅位错超胞的缺陷能级和Moiré扭转双层MoS2的Dirac锥能带色散。电子结构这些实际测试证明该研究提出的机器学习模型对电子哈密顿量的预测具有很高的精度和可迁移性，可以替代DFT用于高效计算大型系统的电子结构。目前，HamGNN的代码已经开源到GitHub网站。
研究领域：电子结构机器学习加速算法
项目网站：https://github.com/QuantumLab-ZY/HamGNN
研究论文：Yang Zhong, Hongyu Yu, Mao Su, Xingao Gong and Hongjun Xiang. Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids. npj Comput. Mater. 9, 182 (2023). 查看PDF
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成果7：基于图网络的晶体结构预测
晶体结构预测是物理、化学以及材料科学领域里的一个基础科学问题。该问题本质上是在结构构型的势能面上寻找全局或局域最小值，因此其包含两个重要的组成部分：一是对不同结构的总能进行定量计算评估从而构建势能面，二是通过优化算法帮助寻找势能面的极值点。传统方法通过使用第一性原理计算构建势能面，计算耗时耗力。该工作提出使用图神经网络，基于给定数据库中的数据，建立晶体结构和能量之间的关联模型，并使用优化算法加速搜索具有最低能量的晶体结构。所用方法的框架（数据库+图神经网络模型+优化算法）是灵活可优化的。对比研究表明，在Matbench 数据库上训练的图神经网络模型与贝叶斯优化相结合，在预测29种典型化合物的晶体结构方面表现出最佳性能，其计算成本比通过密度泛函理论计算筛选结构的传统方法所需的计算成本低三个数量级。该成果于2022年在《Nature Communications》杂志发表题为“Crystal structure prediction by combining graph network and optimization algorithm”的论文。
图: 结合（数据库+图神经网络模型+优化算法）框架的结构预测流程图。
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成果6：场效应晶体管中Si/SiO2界面的极限最小稳定结构预测
场效应晶体管（FET）是半导体芯片的核心部件，其尺寸大小决定了芯片的集成密度。而FET晶体管的终极物理尺寸，很大程度上取决于在栅极处Si/SiO2界面的结构和性能：界面的尺寸过大会导致集成度降低，芯片性能下降；界面的尺寸小，晶体管中源极到漏极之间的电流会受到量子隧穿效应的影响，导致明显的漏电和高的能耗。当前芯片中晶体管的栅极长度（即源极到漏极间的距离）实际上已经达到了几纳米的尺寸，如台积电最新的5nm工艺以及intel最新的10nm工艺，正逐渐逼近硅材料的物理极限，量子隧穿效应导致的漏电已经成为现实工艺困境。因此如何在现代晶体管的设计中充分考虑量子效应影响，在开路电流和闭路漏电之间取得最优的平衡，是关键的科学问题，需要对Si/SiO2界面结构和量子隧穿性能达到新的认知高度。
近期，复旦大学李晔飞和刘智攀提出了一种基于机器学习计算的界面结构预测方法（ML-interface），解决了不同晶体间界面结构预测的难题。该方法基于唯象理论、图论、全局势能面搜索和机器学习势函数等理论计算方法，仅需不同材料体相晶胞的晶体结构作为输入参数，即能可靠预测任意可能界面的原子结构。具体来说，该方法首先采用马氏体晶体学唯象理论（PTMC）筛选出两种晶体相间所有晶格匹配的界面取向关系。然后，通过图论方法产生合理的界面原子模型。最后，采用基于机器学习势函数的全局势能面搜索方法（SSW-NN）确定最稳定的界面结构。应用新发展的ML-interface方法，研究筛选出了所有10个具有短周期性的Si/SiO2界面（图1B）。在这些界面中，除了已经在工业中使用的低密勒（Miller）指数界面Si(100)和Si(110)/SiO2被首次确定了原子结构，还发现了两个新的高密勒指数界面 Si(210)和Si(211)/SiO2。这两个高指数界面具有完美匹配的界面原子结构，优秀的热稳定性，和优秀的电子性质。相对于传统低密勒指数界面具有更高的载流子有效质量，在费米能级附近不具有任何界面态，理论表明可以显著降低载流子量子隧穿达四个数量级，同时界面尺寸也可以小至1纳米，有望实现更短的栅极长度。因此，理论预测新高Miller指数Si(210)和Si(211)/SiO2界面可能是突破Si基半导体性能瓶颈的关键，有望在鳍式场效应晶体管（FinFET）中得到应用。该成果发表在 Physical Review Letters,2022, 128 (22), 226102.
图 （A）ML-Interface方法示意图；（B）预测的10个最稳定Si/SiO2界面结构。
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成果5：基于神经网络势函数的复杂界面缺陷结构研究
界面不仅是诸多器件的重要组成部分，而且蕴含丰富的新物理。确定界面结构是研究界面性质的基础。由于界面两端的体系往往存在晶格适配，使得界面结构非常复杂，不仅包含非常多的原子，而且还可能存在界面缺陷。因此，无论在实验还是理论上，确定界面结构都是一个富有挑战的问题。近年来，神经网络势函数被证明可以大大加快复杂大体系的结构优化，这为解决界面结构问题提供了新手段。2020年，龚新高团队开发了基于神经网络的用于描述CdTe和CdS异质结的势函数，研究了太阳能电池材料的界面结构。在此基础上，该团队进一步将神经网络势函数与自主发展的基于差分演化算法的结构搜索方法相结合，实现了界面缺陷结构的自动搜索。通过研究CdTe/CdS界面，确定了各种生长条件下最稳定的界面缺陷结构，并首次定义了界面缺陷体系的界面能。通过界面缺陷研究，该工作指出贫Cd形成的界面缺陷少，更有利于提升器件效率。该工作表明，机器学习在解决物理器件的实际应用问题方面具有广阔的前景。该成果于2022年在《the Journal of Physical Chemistry C》杂志发表题为“Exploring Large-Lattice-Mismatched Interfaces with Neural Network Potentials: The Case of the CdS/CdTe Heterostructure”的论文。
图：结合神经网络势函数和结构搜索方法确定复杂界面缺陷结构的示意图
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成果4：通用的笛卡尔张量等变预测框架的设计
目前机器学习模型可以很好地拟合材料的能量、带隙等标量属性，但没有显式地考虑晶体在实验室坐标系下的方向信息，因而它们还不能直接预测晶体的依赖方向的张量类性质，如Born有效电荷、介电系数、压电系数等。我们提出了一种通用的笛卡尔张量的等变的参数化公式，其用原子的局域方向信息构造的基底来展开每个原子张量，再用每个原子的张量贡献的平均值来表示晶体的张量。由于利用了每个原子的局域空间信息，这个张量的展开公式是满足旋转等变性的。根据这个笛卡尔张量的参数化公式，我们设计了基于消息传递的ETGNN网络1。通过对几种有机小分子的极化性质的预测，显示出优于FieldSchNet的精度，后者是利用网络的梯度回传来拟合跟极化相关的张量属性的。通过对Born有效电荷、介电系数和压电系数等张量性质的预测，显式了我们设计的张量展开式在各种张量预测上的通用性。并且通过对JARVIS-DFT中约5000种非金属材料的Born有效电荷和介电系数的预测，展现出我们的模型具有很强的泛化和表示能力，可以用一个模型对整个元素周期表中结构进行训练和预测。(Zhong, Y., Yu, H., Gong, X. & Xiang, H. Edge-based Tensor prediction via graph neural networks , arXiv:2201.05770, 2022. 查看PDF)
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成果3：分子和固体的从头紧束缚哈密顿量的等变预测
目前为止，密度泛函理论(DFT)仍然是计算电子结构的主流方法。不过，这种通过自洽迭代来求解系统哈密顿量的方法非常耗时，并且计算量随体系尺寸的扩展性较差。由于原子轨道基底的球谐函数部分随坐标系的旋转会发生等变的转换，因而以原子轨道为基底的电子哈密顿矩阵是等变的。我们设计了E(3)等变的HamNet网络框架2，其输出的哈密顿矩阵满足旋转等变性和宇称对称性。HamNet模型可以在QM9有机小分子数据集、碳同素异形体、Si同素异形体和SiO2异构体的训练集上进行训练然后预测训练集之外的结构的哈密顿量，展现出很强的可转移性。我们对含约3000个硅原子的位错模型的电子结构进行了预测，显式了机器学习电子结构方法在处理大尺寸体系上具有极高的效率。此外，我们提出了一种满足时间反演等变性的含SOC效应的非共线自旋哈密顿量的参数化公式，并在含不同化学计量比的BixSey结构上测试了对SOC哈密顿量预测的精度和可转移性。(Zhong, Y., Yu, H., Su, M., Gong, X. & Xiang, H. Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids, arXiv:2210.16190, 2022 查看PDF)
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成果2：多自由度量子材料的机器学习势能面开发
量子材料通常包括晶格、自旋、电荷、轨道等多个相互耦合的自由度。为了研究实际的量子材料体系，需要发展描述多自由度相互耦合的机器学习势能面方法。我们在磁性、铁电、多铁性等量子材料的机器学习势能面开发方面开展了系统研究，主要成果包括：
（1）发展了通用的磁性神经网络哈密顿量方法来准确描述复杂的磁性。
我们首次提出磁性自旋描述符，并构建了非线性的自旋哈密顿量。可精确描述复杂磁性体系自旋相互作用，通过蒙特卡洛模拟预测磁基态及热力学参数。重现了具有特殊复杂相互作用的自旋模型，还可以很好地描述Fe3GeTe2这个复杂的磁性体系，并可观察到铁磁性、各种螺旋态等等磁态。该工作发表在Phys. Rev. B 105, 174422 (2022)。
（2）发展了考虑自旋晶格耦合的多铁体系的磁性势能面。
开发了考虑时间反演、欧几里得对称性等变的磁性图神经网络势能面，考虑了原子位移自由度、自旋自由度、自旋轨道耦合、非共线磁矩等要素，得到兼顾高精度与高效率的磁性势能面，并实现并行大规模长时间尺度的自旋晶格分子动力学模拟，对于BiFeO3经典多铁体系进行了测试，可以得到实验一致的磁转变温度【arXiv:2203.02853 (2022), arXiv:2211.11403 (2022)】。
（3）发展了基于倒空间的长程相互作用势能面。
开发了基于倒空间开发了考虑全原子相互作用的长程特征方法，从而优化晶体体系的描述符，在有机无机杂化材料数据集上显著提高了能隙等晶体全局性质预测精度，并显著提高了GaN缺陷体系的势能面精度【arXiv:2211.16684 (2022)】。
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成果1：发展了构造有效哈密顿量的机器学习方法
龚新高团队提出了利用机器学习方法筛选重要相互作用项并构造有效哈密顿量的方法（MLMCH方法）。该方法可以在大量待选的相互作用项中高效而可靠地筛选出重要项，从而获得简洁且准确的模型。我们发展的变量筛选方法可视为优化改进的序列浮动前向选择算法。我们的方法不仅考虑在模型中逐个增加项，还考虑了减少、替换项；其中还使用了惩罚因子控制参数个数，并且利用测试集避免过拟合；另外通过采用多种技巧显著提高了搜索效率，使得程序可以在上万个待选相互作用项中高效筛选。
MLMCH方法可应用于磁性、铁电、多铁等类型体系的有效哈密顿量构造。比如，我们用MLMCH方法发现，单层的NiX2（X=Cl, Br 或I）体系中存在显著的双二次交换相互作用（即二体四阶的点乘形式相互作用），而且这种相互作用对于解释NiCl2单层体系的铁磁基态至关重要，该工作发表于Phys. Rev. Lett. 127, 247204。在另一项发表于《Advanced Materials》期刊的工作中，我们采用自旋不变量（spin invariants）来构造Fe3GeTe2（FGT）单层的有效自旋哈密顿量，并利用MLMCH方法筛选重要相互作用项，从而发现了其中多种重要的四阶相互作用；通过蒙特卡洛模拟和分析，我们成功解释了FGT体系中Bloch与Néel型的斯格明子的形成机制。该研究发现的重要高阶相互作用对斯格明子态的旋转手性没有偏好，因此该体系中可以存在多种能量简并或相近的拓扑磁态。
94. Guanjian Cheng, Xin-Gao Gong, Wan-Jian Yin, De novo inverse materials design by combining optimization algorithm, universal potential and universal property model, Preprint at Research Square, 2023 查看PDF
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92. Chen Jiabin[1] ; Li Yang[2] ; Yu Hongyu[3] ; Yang Yali[3] ; Jin Heng[2] ; Huang Bing[4] ; Xiang Hongjun, Light-induced magnetic phase transition in van der Waals antiferromagnets, SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY, 2023 查看PDF
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90. Pei-Lin Kang, Zheng-Xin Yang, Cheng Shang, and Zhi-Pan Liu, Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface, Journal of Chemical Theory and Computation, 2023 查看PDF
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88. Su, M (Su, Mao) [1] , [3] ; Yang, JH (Yang, Ji-Hui) [1] , [2] ; Xiang, HJ (Xiang, Hong-Jun) [1] , [2] ; Gong, XG (Gong, Xin-Gao), Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates, MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2023 查看PDF
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82. He Guangmeng ; Zhang Huimin; Ni Jinyang Liu Boyu; Xu Changsong ; Xiang Hongjun, Microscopic Magnetic Origin of Rhombohedral Distortion in NiO, CHINESE PHYSICS LETTERS, 2023 查看PDF
81. Zhang, P (Zhang, Pan) [1] , [2] ; Shang, C (Shang, Cheng) [2] , [3] ; Liu, ZP (Liu, Zhipan) [2] , [3] ; Yang, JH (Yang, Ji-Hui) [1] , [2] ; Gong, XG (Gong, Xin-Gao) , Origin of performance degradation in high-delithiation LixCoO2: insights from direct atomic simulations using global neural network potentials, JOURNAL OF MATERIALS CHEMISTRY A, 2023
80. Zhang, HM (Zhang, Huimin) [1] , [2] , [3] ; Zhong, Y (Zhong, Yang) [1] , [2] , [3] ; Ouyang, CY (Ouyang, Chuying) [4] ; Gong, XG (Gong, Xingao) [1] , [2] , [3] ; Xiang, HJ (Xiang, Hongjun), Theoretical study on the magnetic properties of cathode materials in the lithium-ion battery, JOURNAL OF CHEMICAL PHYSICS, 2023 查看PDF
79. Shang, C.,Liu, Z. P., Constructing machine learning potentials with active learning, Quantum Chemistry in the Age of Machine Learning (Book), 2023 查看PDF
78. Dongxiao Chen, Cheng Shang, Zhi-Pan Liu, Machine-Learning Atomic Simulation for Heterogeneous Catalysis, npj Computational Materials, 2023 查看PDF
77. Jili Li, Ye-Fei Li*, Zhi-Pan Liu, In situ Structure of Mo-doped Pt-Ni Catalyst during Electrochemical Oxygen Reduction Resolved from Machine Learning Based Grand Canonical Global Optimization, JACS Au 2023, 3, 4, 1162–1175, 2023 查看PDF
76. Ke-Xiang Zhang Zhi-Pan Liu, Electrochemical Hydrogen Evolution on Pt-based Catalysts from A Theoretical Perspective , J. Chem. Phys. 158, 141002 (2023) 查看PDF
75. Ji, Junyi; Yu, Guoliang; Xu, Changsong; Xiang, H J, General Theory for Bilayer Stacking Ferroelectricity, PHYSICAL REVIEW LETTERS, 2023
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72. Hairui Bao, Bao Zhao, Jiayong Zhang, Yang Xue, Tong Zhou and Zhongqin Yang, Quantum anomalous Hall effect with high Chern numbers in functionalized square-octagon Sb monolayers, 2D Mater. 10 035004, 2023 查看PDF
71. Lu Liu, Ke Yang, Guangyu Wang, Di Lu, Yaozhenghang Ma, and Hua Wu, Contrasting electronic states of RuI3 and RuCl3, Phys. Rev. B 107, 165134, 2023 查看PDF
70. Guangyu Wang, Ke Yang, Yaozhenghang Ma, Lu Liu, Di Lu, Yuxuan Zhou, and Hua Wu, Superexchange Interactions and Magnetic Anisotropy in MnPSe3 Monolayer, Chin. Phys. Lett. 40, 077301, 2023 查看PDF
69. Tang, QY ; Yang, JH ; Liu, ZP ; Gong, XG, Directly Determining the Interface Structure and Band Offset of a Large-Lattice-Mismatched CdS/CdTe Heterostructure, CHINESE PHYSICS LETTERS, 2020 查看PDF
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67. Bayaraa, T ; Xu, CS; Yang, YL; Xiang, HJ (Xiang, Hongjun) ; Bellaiche, L, Magnetic-Domain-Wall-Induced Electrical Polarization in Rare-Earth Iron Garnet Systems: A First-Principles Study, Physical Review Letters, 2020
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65. Hongjun Xiang, Towards two-dimensional room temperature multiferroics, National Science Review 7, 1844, 2020
64. Dan-Dong Wang, Xin-Gao Gong, Ji-Hui Yang, Unusual interlayer coupling in layered Cu-based ternary chalcogenides CuMCh2(M=Sb, Bi; Ch=S, Se), Nanoscale, 2021
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62. Jing Li, JunSheng Feng, PanShuo Wang,ErJun Kan, and HongJun Xiang, Nature of spin-lattice coupling in two-dimensional CrI3 and CrGeTe3, Science China Physics Mechanics & Astronomy 64, 286811, 2021 查看PDF
61. Yali Yang, Feng Lou, and Hongjun Xiang, Cooperative nature of ferroelectricity in two-dimensional hybrid organic–inorganic perovskites, Nano Lett. 21, 3170, 2021 查看PDF
60. Xueyang Li,Hongyu Yu, Feng Lou, Junsheng Feng, Myung-Hwan Whangbo, and Hongjun Xiang, Spin Hamiltonians in Magnets: Theories and Computations, Molecules 26, 803, 2021 查看PDF
59. J. Y. Ni, X. Y. Li, D. Amoroso, X. He, J. S. Feng, E. J. Kan, S. Picozzi, and H. J. Xiang, Giant Biquadratic Exchange in 2D Magnets and Its Role in Stabilizing Ferromagnetism of Monolayers, Phys. Rev. Lett. 127, 247204, 2021 查看PDF
58. Pan Zhang, Ji-Hui Yang, and Xin-Gao Gong, Unusual defect properties in multivalent perovskite Cs2Au2I6: A first-principles study, Phys. Rev. Mater. 5, 085405, 2021 查看PDF
57. Ke Yang, Guangyu Wang, Lu Liu, Di Lu, and Hua Wu, Triaxial magnetic anisotropy in the two-dimensional ferromagnetic semiconductor CrSBr, Phys.Rev. B 104, 144416, 2021 查看PDF
56. Guangyu Wang, Lu Liu, Ke Yang, and Hua Wu, CrSbSe3: A pseudo one-dimensional ferromagnetic semiconductor, Phys. Rev. Mater. 5, 124412, 2021 查看PDF
55. Luo, L. H.,Huang, S. D.,Shang, C.,Liu, Z. P., Resolving Activation Entropy of CO Oxidation under the Solid-Gas and Solid-Liquid Conditions from Machine Learning Simulation, ACS Catalysis, 2022, 12, 6265, 2021 查看PDF
54. H. Huan, Y. Xue, B. Zhao, G. Y. Gao, H. R. Bao, and Z. Q. Yang, Strain-induced half-valley metals andtopological phase transitions in MBr2 monolayers (M = Ru, Os), Phys. Rev. B 104, 165427 (2021), 2021 查看PDF
53. L. Liu, B. Zhao, J. Y. Zhang, H. R. Bao, H. Huan, Y. Xue, Y. Li, and Z. Q. Yang, Prediction of coexistence of anomalous valley Hall and quantum anomalous Hall effects in breathing kagome-honeycomb lattices, Phys. Rev. B 104,165427 (2021), 2021 查看PDF
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49. Menglin Huang, Shuaicheng Lu, Kanghua Li, Yue Lu, Chao Chen, Jiang Tang, Shiyou Chen, p-Type Antimony Selenide via Lead Doping, Solar RRL, 2021 查看PDF
48. Zenghua Cai, Yuning Wu, Shiyou Chen, Energy-dependent knock-on damage of organic–inorganic hybrid perovskites under electron beam irradiation: First-principles insights, Applied Physics Letters, 2021 查看PDF
47. Gu H Y, Gao W, Gong X G. , Hyperdynamics simulations with ab initio forces, JOURNAL OF CHEMICAL PHYSICS, 2021
46. Guo-Jun Zhu, Yong-Gang Xu, Xin-Gao Gong, Ji-Hui Yang, and Boris I. Yakobson, Dimensionality-Inhibited Chemical Doping in Two-Dimensional Semiconductors: The Phosphorene and MoS2 from Charge Correction Method, Nano Lett. 21, 6711 , 2021 查看PDF
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44. Yu-ang Fan, Yingcheng Li, Yuting Hu, Yishan Li, Xinyue Long, Hongfeng Liu, Xiaodong Yang, Xinfang Nie, Jun Li, Tao Xin, Dawei Lu, Yidun Wan, Experimental realization of a topologically protected Hadamard gate via braiding Fibonacci anyons, arXiv:2210.12145 (Science under review), 2022 查看PDF
43. Yu Zhao, Shan Huang, Hongyu Wang, Yuting Hu, Yidun Wan, Exactly solvable Hamiltonian model of the doubled Ising and toric code topological phases separated by a gapped domain wall via anyon condensation, arXiv:2209.12750 (JHEP under review), 2022 查看PDF
42. Zichang Huang, Shan Huang, Yidun Wan, A saddle-point finder and its application to the spin foam model, arXiv:2206.11874 (PRD under review), 2022 查看PDF
41. Yuting Hu, Zichang Huang, Ling-Yan Hung, and Yidun Wan, Anyon condensation: coherent states, symmetry enriched topological phases, Goldstone theorem, and dynamical rearrangement of symmetry, J. High Energ. Phys. 2022, 26 (2022), 2022 查看PDF
40. Hongyu Wang, Yuting Hu, and Yidun Wan, Extend the Levin-Wen model to two-dimensional topological orders with gapped boundary junctions, J. High Energ. Phys. 2022, 88 (2022), 2022 查看PDF
39. Changsong Xu, Xueyang Li, Peng Chen, Yun Zhang, Hongjun Xiang*, L. Bellaiche*, Assembling diverse skyrmionic phases in Fe3GeTe2 monolayer: the role of multiple fourth order interactions, Adv. Mater. 34,2107779 (2022), 2022 查看PDF
38. Hongyu Yu#, Changsong Xu#, Xueyang Li, Feng Lou, L. Bellaiche, Zhenpeng Hu, Xingao Gong, and Hongjun Xiang*, Complex spin Hamiltonian represented by an artificial neural network, Physical Review B 105,174422(2022), 2022 查看PDF
37. Guangmeng He#, Huimin Zhang#, Jinyang Ni, Boyu Liu, Changsong Xu*, and Hongjun Xiang*, Microscopic Magnetic Origin of Rhombohedral Distortion in NiO, Chinese Physics Letters 39,067501(2022), 2022 查看PDF
36. Shiqing Deng#, Changsong Xu#, Shaobo Cheng, Wenbin Wang, Jing Zhu*, Yimei Zhu*, Jun Chen*, Self-Assembled LuFeO3/LuFe2O4 Heterostructure with Emergent Ferroic Orderings, Advanced Functional Materials ,202206050(2022), 2022 查看PDF
35. Wang J, Pan W, Sun DY., Efficient world-line-based quantum Monte Carlo method without Hubbard–Stratonovich transformation. , Scientific Reports 2022, 12(1): 8251., 2022 查看PDF
34. Zhao D, Liu F, Duan X, Sun D., Intrinsic disorder of dangling OH-bonds in the first water layer on noble metal surfaces. , Computational Materials Science 2022, 201: 110863., 2022 查看PDF
33. Zhang DM, Sun DY, Gong XG., Angell plot from the potential energy landscape perspective., Physical Review 2022, 106(6): 064129., 2022 查看PDF
32. Di Lu, Lu Liu, Yaozhenghang Ma, Ke Yang, and Hua Wu, A unique electronic state in a ferromagnetic semiconductor FeCl2 monolayer, J. Mater. Chem. C 10, 8009-8014, 2022 查看PDF
31. Ke Yang, Wenjing Xu, Di Lu, Yuxuan Zhou, Lu Liu, Yaozhenghang Ma, Guangyu Wang, and Hua Wu, Magnetic frustration in the cubic double perovskite Ba2NiIrO6, Phys.Rev. B 105, 184413, 2022 查看PDF
30. Di Lu, Ke Yang, Lu Liu, Guangyu Wang, and Hua Wu, Spin–Orbital States and Strong Antiferromagnetism of Layered Eu2SrFe2O6 and Sr3Fe2O4Cl2, Inorg. Chem. 61, 12692–12697, 2022 查看PDF
29. Zhongjie Wang, Lu Liu, Haoran Zheng, Meng Zhao, Ke Yang, Chunzheng Wang, Fang Yang, Hua Wu, and Chunlei Gao, Direct observation of the Mottness and p–d orbital hybridization in the epitaxial monolayer α-RuCl3, Nanoscale, 14, 11745-11749, 2022 查看PDF
28. Yuqiang Fang, Ke Yang, Enze Zhang, Shanshan Liu, Zehao Jia, Yuda Zhang, Hua Wu, Faxian Xiu, and Fuqiang Huang, Quasi-1D van der Waals Antiferromagnetic CrZr4Te14 with Large In-Plane Anisotropic Negative Magnetoresistance , Adv. Mater. 34, 2200145, 2022 查看PDF
27. Dan-Dong Wang, Xin-Gao Gong, Ji-Hui Yang, Semiconductor-to-metal transition from monolayer to bilayer blue phosphorous induced by extremely strong interlayer coupling: a first-principles study, Nanoscale 14, 4082, 2022 查看PDF
26. Mao Su, Ji-Hui Yang, Zhi-Pan Liu, Xin-Gao Gong, Exploring Large-Lattice-Mismatched Interfaces with Neural Network Potentials: The Case of the CdS/CdTe Heterostructure, J. Phys. Chem. C 126, 13366, 2022 查看PDF
25. Shanshan Wang, Menglin Huang, Yu-Ning Wu, Weibin Chu, Jin Zhao, Aron Walsh, Xin-Gao Gong, Su-Huai Wei, Shiyou Chen, Effective lifetime of non-equilibrium carriers in semiconductors from non-adiabatic molecular dynamics simulations, Nature Computational Science 2, 486, 2022 查看PDF
24. Shiru Song, Ji-Hui Yang, Xin-Gao Gong, Abnormally weak intervalley electron scattering in MoS2 monolayer: insights from the matching between electron and phonon bands, Nanoscale 14, 12007, 2022 查看PDF
23. Hao Wu, Yi-Lin Zhang, Zhi-Xin Guo, Xin-Gao Gong, Anomalous high thermal conductivity in heavy element compounds with van der Waals interaction, Appl. Phys. Lett. 121, 182204, 2022 查看PDF
22. Yong-Gang Xu, Pan Zhang, Guo-Jun Zhu, Ji-Hui Yang, Xin-Gao Gong, Enhancing Hole Density and Suppressing Recombination Centers through Illumination in Kesterite Thin Film Solar Cells, J. Phys. Chem. Lett. 13, 11, 2474, 2022 查看PDF
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3. Menglin Huang, Shanshan Wang, Tao Zhang, and Shiyou Chen, Searching for Band-Dispersive and Defect-Tolerant Semiconductors from Element Substitution in Topological Materials, Journal of the American Chemical Society, 2022 查看PDF
2. Shanshan Wang, Menglin Huang, Yu-Ning Wu, and Shiyou Chen, Formation of Bi−Bi Dimers in Heavily Bi-Doped Lead Halide Perovskites: Origin of Carrier Density Saturation, Physical Review Applied, 2022 查看PDF
1. Yingcheng Li, Tao Xin, Chudan Qiu, Keren Li, Gangqin Liu, Jun Li, Yidun Wan, Dawei Lu, Dynamical-invariant-based holonomic quantum gates: Theory and experiment, Fundamental Research, 2022, 2022 查看PDF
