1 citations · 1 across the 6 of their papers we have counts for
6 papers
Overcoming Orchestration Bottlenecks at Exascale: A Decentralized, Policy-Driven Approach for Sim-AI Ensembles
Harikrishna Tummalapalli, Christine M. Simpson, Riccardo Balin +4
Scientific computing is increasingly shifting from monolithic applications to coupled simulation-AI workflows composed of highly heterogeneous tasks with diverse hardware, scale, a…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Vapor-liquid-solid growth of unconventional nanowires
Thang Pham, Arindom Nag
Vapor-liquid-solid (VLS) growth has delivered a level of synthetic control in group IV and III-V semiconductor nanowires that remains unmatched in any other class of one-dimensiona…
Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System
Thang Duc Pham, Harikrishna Tummalapalli, Fakhrul Hasan Bhuiyan +5
The integration of Artificial Intelligence (AI) with High-Performance Computing (HPC) is transforming scientific workflows from human-directed pipelines into adaptive systems capab…
ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation
Vitor F. Grizzi, Thang Duc Pham, Luke N. Pretzie +3
Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure, but large comput…
ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Thang D. Pham, Aditya Tanikanti, Murat Keçeli
Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However,…