arXiv subjects

Jin-Soo Kim

Publications and source records attributed to Jin-Soo Kim.

3 recordsLinked to original sources

arXiv2026

Rethinking the Role of Homogeneous Surface Models in High-Entropy Alloy Catalyst Screening

High-entropy alloy (HEA) catalysts are commonly modeled as homogeneously mixed surfaces, although thermodynamic driving forces can produce surface segregation and chemical ordering. Here, we test the validity and practical limits of this approximation by comparing random homogeneous slabs with surfaces annealed using cluster-expansion Monte Carlo (CEMC) against three published high-throughput composition-activity datasets for the oxygen reduction and hydrogen evolution reactions. Monte Carlo-based uncertainty quantification simulations propagated uncertainties in catalyst composition and predicted binding energies through 10,000 trials. CEMC generally improved agreement with experiment, but the advantage is not robust, and neither model reliably reproduces experimental composition-activity map. Structural analysis revealed surface segregation, rather than short-range ordering, is the primary origin of the difference between the models. Nevertheless, the element-conditioned binding-energy ranges of the two models largely overlapped, supporting the use of homogeneous slabs for rapid screening of elemental combinations.

cond-mat.mtrl-sci↗

arXiv2026

SCENIC: Stream Computation-Enhanced SmartNIC

Although modern, AI-centric datacenters heavily rely on SmartNICs, existing devices impose a hard trade-off. Commercial SmartNICs provide high bandwidth and easy software integration, but offer limited support for customization and data processing offload. In contrast, research SmartNICs often suffer from low bandwidth, limited functionality, and poor software compatibility -- to the point that many are not actual NICs in a technical sense. This gap can be closed by treating the NIC datapath as a first-class stream computation substrate with shared hardware/software abstractions for a tight co-design of infrastructure and applications. To demonstrate this, we introduce SCENIC, an open-source datacenter SmartNIC. SCENIC implements a 200G network datapath over offloaded TCP/IP and RDMA stacks, as well as a fallback path for processing arbitrary network traffic. On top of the network logic, SCENIC combines on-datapath Stream Compute Units (SCUs) for data processing and embedded ARM cores for flexible control path manipulation with direct access to GPUs and SSDs. SCENIC is fully integrated with the OS, exposing native Linux network and RDMA verb interfaces, making the programmable datapath transparent to existing applications while enabling control of, e.g., user-defined offloads and programmable congestion control. SCENIC's performance matches commercial platforms, and we show its versatility through several use cases such as offloaded collective communication and network-to-GPU hash-based data partitioning.

cs.AR↗

arXiv2020

Development of Open Informal Dataset Affecting Autonomous Driving

This document is a document that has written procedures and methods for collecting objects and unstructured dynamic data on the road for the development of object recognition technology for self-driving cars, and outlines the methods of collecting data, annotation data, object classifier criteria, and data processing methods. On-road object and unstructured dynamic data were collected in various environments, such as weather, time and traffic conditions, and additional reception calls for police and safety personnel were collected. Finally, 100,000 images of various objects existing on pedestrians and roads, 200,000 images of police and traffic safety personnel, 5,000 images of police and traffic safety personnel, and data sets consisting of 5,000 image data were collected and built.

cs.CV↗