Cu-C materials and interfaces are central to diamond thermal management, metallization, graphene growth and carbon-coating technologies, but atomistic simulations across these applications require a potential that describes metallic Cu, diverse carbon bonding, heterogeneous Cu-C interactions and close atomic encounters. Here we develop NEP-CuC, a transferable neuroevolution potential trained on a dataset of 2,727 uniformly relabelled configurations selected from a heterogeneous Cu-C candidate pool. Disagreement within an ensemble of five independently trained models provides an internal indicator of model sensitivity. A Ziegler-Biersack-Littmark (ZBL) correction describes short-range nuclear repulsion, while a runtime D3(BJ) correction supplements dispersion-sensitive calculations. Tests on source-derived held-out and independent external datasets, together with structural, elastic, defect, interfacial, vibrational and finite-temperature transport benchmarks, establish broad but property-dependent physical fidelity across elemental Cu, diverse carbon environments and heterogeneous Cu-C configurations. Large-scale simulations of amorphous-carbon formation and orientation-dependent shock compression of Cu further test transferability under strongly non-equilibrium conditions. For Cu/diamond interfaces, the same potential describes deposition, annealing and tensile response: preparation history leaves persistent differences in interphase structure, while the mean peak nominal tensile stress at 500 K decreases from $12.35\pm0.71$ to $6.65\pm0.98$ GPa as the incident Cu energy during the initial deposition stage increases from 1 to 50 eV. NEP-CuC provides an atomistic framework for linking processing, interfacial structure and physical response across Cu-C systems, with direct relevance to Cu/diamond thermal-management interfaces, diamond metallization and non-equilibrium Cu-C processing.
Fast Transfer-free Synthesis of High-quality Monolayer Graphene on Insulating Substrates by Simple Rapid Thermal Treatment
在给定的电介质衬底上无转移合成高质量、大面积石墨烯，这对于器件应用是非常理想的，仍然是一个重大的挑战。在本文中，我们报告了一种简单的快速热处理方法，用于从固体碳源在SiO2/Si衬底上快速直接生长高质量，大规模的单层石墨烯。在RTT工艺中采用了固体碳层/铜膜/SiO2的堆叠结构。插入的铜膜不仅充当碳前体的活性催化剂，而且还充当防止过早碳溶解的“过滤器”，因此有助于在SiO2/Si上生长单层石墨烯。所产生的单层石墨烯在室温下表现出高达3000 cm 2 V-1 s-1的高载流子迁移率和标准半整数量子振荡。
Revealing the Atomic-Scale Structure of the Copper Sulfuric Acid Interface
Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；Gyeongsang National University（庆尚国立大学）；Imperial College London（伦敦帝国理工学院）；University of Toronto（多伦多大学）；Univ Rouen Normandie, CNRS, INSA Rouen Normandie, Groupe de Physique des Matériaux（鲁昂诺曼底大学，法国国家科学研究中心，鲁昂诺曼底国立应用科学学院，物质物理研究组）；KU Leuven（荷语鲁汶大学）
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
Graz University of Technology（格拉茨工业大学）；Know-Center GmbH, Research Center for Data-Driven Business & Big Data Analytics（知识中心有限公司（数据驱动业务与大数据分析研究中心））；Università di Roma La Sapienza（罗马第一大学）
Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design
