Towards knowledge-based workflows: a semantic approach to atomistic simulations for mechanical and thermodynamic properties
Institute for Advanced Simulations – Materials Data Science and Informatics (IAS‑9)（先进模拟研究所——材料数据科学与信息学研究所）；Forschungszentrum Jülich GmbH（吕贝克研究所）；Institute of Metallurgy - Chair of Computational Material Sciences/Engineering（冶金研究所——计算材料科学与工程教授席位）；Technical University of Clausthal（克莱斯特哈尔技术大学）；Interdisciplinary Centre for Advanced Materials Simulation (ICAMS)（先进材料模拟跨学科中心）；Ruhr-Universität Bochum（博德姆鲁尔-大学）；Bundesanstalt für Materialforschung und -prüfung（联邦材料研究所）
Abril Azocar Guzman, Hoang-Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld
Mechanical and thermodynamic properties, including the influence of crystal defects, are critical for evaluating materials in engineering applications. Molecular dynamics simulations provide valuable insight into these mechanisms at the atomic scale. However, current practice often relies on fragmented scripts with inconsistent metadata and limited provenance, which hinders reproducibility, interoperability, and reuse. FAIR data principles and workflow-based approaches offer a path to address these limitations. We present reusable atomistic workflows that incorporate metadata annotation aligned with application ontologies, enabling automatic provenance capture and FAIR-compliant data outputs. The workflows cover key mechanical and thermodynamic quantities, including equation of state, elastic tensors, mechanical loading, thermal properties, defect formation energies, and nanoindentation. We demonstrate validation of structure-property relations such as the Hall-Petch effect and show that the workflows can be reused across different interatomic potentials and materials within a coherent semantic framework. The approach provides AI-ready simulation data, supports emerging agentic AI workflows, and establishes a generalizable blueprint for knowledge-based mechanical and thermodynamic simulations.
El Agente Potente: High-Throughput Agentic Atomistic Simulations
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Acceleration Consortium（加速联盟）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））；NVIDIA（英伟达）
Harnessing AtomisticSkills for Agentic Atomistic Research
Department of Materials Science；Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea；Harvard University, Department of Chemistry；Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA；Department of Nuclear Science；Shell Information Technology International Inc., Texas 77082, United States；Shell International Exploration \& Production Inc., Texas 77079, United States
Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
Princeton University（普林斯顿大学）；University of Southampton（南安普顿大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Riven Systems；Federal Institute of Materials Research and Testing（联邦材料研究与测试研究所）；Globus, University of Chicago（芝加哥大学 Globus）；Instituto de Nanociencia y Materiales de Aragón, CSIC-Universidad de Zaragoza（阿拉贡纳米科学与材料研究所，CSIC-萨拉戈萨大学）；Argonne National Laboratory（阿贡国家实验室）；The University of Chicago（芝加哥大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Harvard 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）；D-Wave Quantum Inc.（D-Wave量子公司）；R. F. Smith School of Chemical and Biomolecular Engineering, Cornell University（康奈尔大学 R.F. 史密斯化学与生物分子工程学院）；Massachusetts Institute of Technology（麻省理工学院）；Flatiron Institute（平塔研究所）；Elemynt Pte. Ltd.（Elemynt私人有限公司）；Washington University in St. Louis（圣路易斯华盛顿大学）
Atomistic Modeling of Chemical Disorder in Materials: Bridging Conventional Methods and AI-Assisted Approaches
Department of Materials Design and Innovation, University at Buffalo（布法罗大学材料设计与创新系）；Department of Materials Science and Engineering, National University of Singapore（新加坡国立大学材料科学与工程系）
Atomistic Investigation of Elementary Dislocation Properties Influencing Mechanical Behaviour of $Cr_{15}Fe_{46}Mn_{17}Ni_{22}$ alloy and $Cr_{20}Fe_{70}Ni_{10}$ alloy
Atomistic insights into the structural, thermal, and mechanical evolution of $Zr_{47.5}Cu_{47.5}Ag_{5}$ bulk metallic glass
Instituto Sabato, UNSAM, CNEA（萨巴托研究所，阿根廷国家原子能委员会）；Departamento de Materia Condensada, CNEA（凝聚态物质部门，阿根廷国家原子能委员会）；Department of Mathematical Sciences, UTEP（数学科学系，埃尔帕索大学）
Unlocking device-scale atomistic modelling of phase-change memory materials
量子精确的计算机模拟在理解先进存储技术的相变材料（PCM）方面发挥着核心作用。然而，直接量子力学模拟必然限于简化模型，包含不超过几百或一千个原子。基于机器学习（ML）的潜在模型在量子力学数据上进行“训练”，是一种新兴的替代方法，目前正在从高度专业化的模拟工具发展到更广泛应用的模拟工具。在这里，我们表明，一个通用的，成分灵活的ML模型可以描述广泛的旗舰Ge-Sb-Te PCM在真实的设备条件下，包括非等温加热和化学无序，这是相关的存储器应用。ML模型的速度使多个热循环的原子模拟和神经启发计算的精细操作成为可能，即累积SET和迭代SET。一个设备规模的能力演示（40 x 20 x 20 nm 3）表明，新的ML潜力可以直接描述基于PCM的存储器产品中的技术相关过程。在更广泛的背景下，我们的工作展示了ML驱动的材料模拟现在如何进入一个可以指导高性能电子设备架构设计的阶段。
Atomistic Framework for Glassy Polymer Viscoelasticity Across Twenty Frequency Decades
University of Milan（米兰大学）；University of Göttingen（哥廷根大学）；Graz University of Technology（格拉茨工业大学）；Polymer Competence Center Leoben GmbH（莱奥本聚合物能力中心有限公司）；US Army DEVCOM Army Research Laboratory（美国陆军DEVCOM陆军研究实验室）
Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
Engineering Division, Oak Ridge National Laboratory（橡树岭国家实验室工程部）；Mathematics Division, Oak Ridge National Laboratory（橡树岭国家实验室数学部）；National Center for Computational Sciences Division, Oak Ridge National Laboratory（橡树岭国家实验室国家计算科学中心部）
Device-Scale Atomistic Simulations of Heat Transport in Advanced Field-Effect Transistors
Bohai University（渤海大学）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）；Huazhong University of Science and Technology（华中科技大学）；Jiangnan University（江南大学）；Beijing Institute of Technology（北京理工大学）
