The mechanical properties of metal-organic frameworks (MOFs) are of high fundamental and also practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has rarely been used for highly complex materials like MOFs. In the present contribution, we apply this technique to study a newly synthesized MOF-type material, referred to as GUT2. We show that when combining the experiments with state-of-the-art simulations of elastic properties and phonon bands (based on machine-learned force fields and dispersion-corrected density-functional theory). This provides a comprehensive understanding of the experimental signals, which are correlated with the longitudinal and transverse sound velocities. Moreover, even when dealing with comparably small single crystals, which limit the range of accessible experimental data, combining the insights from simulations and experiments allows the determination of approximate values for the components of the elastic tensor of the studied material.
Evaluating Mechanical Property Prediction across Material Classes using Molecular Dynamics Simulations with Universal Machine-Learned Interatomic Potentials
School of Physics, Chemistry and Earth Sciences, The University of Adelaide（阿德莱德大学物理、化学与地球科学学院）
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
Universidad Autónoma de Madrid（马德里自治大学）；Aalto University（阿尔托大学）；Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid（马德里自治大学凝聚态物理中心（IFIMAC））；Nano Life Science Institute (WPI-NanoLSI), Kanazawa University（金泽大学纳米生命科学研究院（WPI-NanoLSI））
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
Center for Quantum Information and Quantum Biology, The University of Osaka（大阪大学量子信息与量子生物学中心）；Graduate School of Science, The University of Osaka（大阪大学理学研究科）
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Bohai University（渤海大学）；The Chinese University of Hong Kong（香港中文大学）；Chalmers University of Technology（查尔姆斯理工大学）；Shenzhen Technology University（深圳职业技术大学）；Shenzhen University（深圳大学）；Xinyu University（新余学院）；University of Science and Technology Beijing（北京科技大学）
Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
Department of Materials Science and Engineering, Seoul National University（首尔大学材料科学与工程系）；Research Institute of Advanced Materials, Seoul National University（首尔大学先进材料研究所）；Department of Computer Science and Engineering, Seoul National University（首尔大学计算机工程系）；AI Center, Samsung Electronics（三星电子人工智能中心）；Center for AI and Natural Sciences, Korea Institute of Advanced Study（韩国高级研究院人工智能与自然科学中心）
Two-Dimensional Semiconducting Metal Organic Frameworks with Auxetic Effect, Room Temperature Ferrimagnetism, Chiral Ferroelectricity, Bipolar Spin Polarization and Topological Nodal Lines/Points
Towards Accurate and Scalable High-throughput MOF Adsorption Screening: Merging Classical Force Fields and Universal Machine Learned Interatomic Potentials
Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Università degli Studi di Roma La Sapienza（罗马第一大学）；Science and Technology Facilities Council（科学与技术设施委员会）；Argonne National Laboratory（阿贡国家实验室）
Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
Cavendish Laboratory, Department of Physics, University of Cambridge（卡文迪许实验室，物理系，剑桥大学）；Lennard-Jones Centre, University of Cambridge（兰纳德-琼斯中心，剑桥大学）；Yusuf Hamied Department of Chemistry, University of Cambridge（尤苏夫·哈米德化学系，剑桥大学）
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Peking University（北京大学）；AI for Science Institute（人工智能科学研究院）；Tsinghua University（清华大学）；HEDPS, CAPT, College of Engineering, Peking University（北京大学工学院理论力学与计算力学系、高超声速空气动力学国家重点实验室）；DP Technology（深势科技）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）；National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics（中国科学院应用物理与计算数学研究所）
Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
Institute for Materials Science and Max Bergmann Center of Biomaterials, TUD Dresden University of Technology（德累斯顿工业大学材料科学与马克斯·伯格曼生物材料中心）；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；Cluster of Excellence PhoenixD, Leibniz Universität Hannover（汉诺威莱布尼茨大学凤凰D卓越集群）；Dresden Center for Computational Materials Science (DCMS), TUD Dresden University of Technology（德累斯顿计算材料科学中心（DCMS），德累斯顿工业大学）；Cluster of Excellence CARE, TU Dresden and RWTH Aachen（Aachen工业大学与德累斯顿工业大学CARE卓越集群）；Cluster of Excellence CeTI, TU Dresden（德累斯顿工业大学CeTI卓越集群）
Self-organized Kagome-lattice in a metal-organic monolayer
我们成功地在表面合成金属有机共价配位网络与密集的可果美晶格的金属中心。在Mn中心的情况下，从头计算表明，在Ag（111）上的吸附单分子层具有严格的二维（2D）铁磁Kagome金属的所有特征。四羟基醌（THQ）和金属原子（M=Cu或Mn）共沉积在Ag（111）衬底上以构建良好有序的二维晶格M $_3$ C $_6$ O $_6$。通过扫描隧道显微镜（STM）、低能电子衍射（LEED）和X射线光电子能谱学（XPS）研究表面以优化生长条件如助熔剂和温度。通过密度泛函理论（DFT）计算，阐明了原子、电子和磁性结构的细节。XPS和DFT揭示了Cu的$^+$电荷状态和没有本地磁矩的铜有机网络。对于Mn-有机网络，我们发现电荷态Mn $^{2+}$和局部自旋S=5/2。电荷转移使Cu $^+$和Mn $^{2+}$电荷状态稳定。我们发现了M $_3$ C $_6$ O $_6$晶格的两种不同的变体。忽略小的自旋-轨道耦合的DFT计算显示狄拉克点，即在布里渊区的K点处与线性电子色散交叉的带。如果没有电荷转移，则该狄拉克点处于费米能级，但是如果确认Ag（111）表面上的Cu $_3$ C $_6$ O $_6$的电子掺杂，则该狄拉克点下降100 meV。我们预测一个孤立的M $_3$ C $_6$ O $_6$单层的磁耦合是短程和反铁磁的，导致高挫折在可果美晶格和自旋液体基态的趋势。在空穴从衬底转移…
A practical guide to machine learning interatomic potentials -- Status and future
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Harvard University（哈佛大学）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Utrecht University（乌得勒支大学）；Globus（格洛布斯公司）；Argonne National Laboratory（阿贡国家实验室）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；National Institute of Standards and Technology（美国国家标准与技术研究院）；University of Cambridge（剑桥大学）；Google DeepMind（谷歌DeepMind）
