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DTSTAMP:20260421T090513Z
LOCATION:Bldg. 8 - Room B 102
DTSTART;TZID=Europe/Stockholm:20260701T100000
DTEND;TZID=Europe/Stockholm:20260701T103000
UID:submissions.pasc-conference.org_PASC26_sess157_msa138@linklings.com
SUMMARY:Self-Validating Research Assistant for High Performance Electronic
  Structure Calculations
DESCRIPTION:Luigi Genovese (CEA Grenoble)\n\nAgentic AI systems based on l
 arge language models (LLMs) offer promising avenues for automating atomist
 ic modeling workflows on high-performance computing (HPC) platforms. Howev
 er, incorrect workflow specification in this context can lead to substanti
 al computational waste and unreliable scientific results.\nIn this work, w
 e show that realistic atomistic workflows require validated code generatio
 n rather than static tool invocation.  We introduce the main ideas that ar
 e leading us to the development of  LARA, a multi-phase architecture that 
 combines retrieval-augmented generation over local scientific documentatio
 n with multi-level validation, including mandatory dry-run execution prior
  to HPC submission. By explicitly separating scientific understanding, cod
 e generation, validation, and review, LARA enables flexible yet safe autom
 ation of complex workflows. We argue that dry-run validation is a prerequi
 site for integrating agentic AI with laboratory-developed HPC codes and re
 presents a key abstraction for trustworthy AI-assisted scientific computin
 g.\n\nDomain: Chemistry and Materials, Climate, Weather, and Earth Science
 s, Engineering, Physics\n\nSession Chair: Jan Janssen (Max Planck Institut
 e for Sustainable Materials)\n\n
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