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AI/ML Model Development (Graph Neural Networks)

Active

Federal

Response Deadline02/16/2027, 12:00:00 AM UTC

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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This subcontract, managed by Triad for the Department of Energy, focuses on the acceleration and optimization of the SCACS platform for DOE technology licensees. The project involves implementing Graph Neural Networks to speed up atomic-site-projected conductivity methods, utilizing million-atom microstructure datasets to train models and optimizing AI inference for physics-grounded predictions. The final deliverable is an optimized and accelerated AI simulation engine. The work will be performed in Los Alamos, New Mexico, and requires the use of High-Performance Computing clusters and GPU acceleration frameworks. The opportunity was posted on August 18, 2026, with a response deadline of February 16, 2027, falling under NAICS code 541511.

General Info

Triad subcontract for DOE to develop an accelerated AI simulation engine for SCACS.

Agency

Department Of Energy → Triad - DOE ContractorView Agency

NAICS

541511 - Custom Computer Programming ServicesView NAICS

Place of Performance

Los Alamos, NM, 87545, USA

Set-Aside

NONE

Documents

This scope was carved out of S-196281.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Simulator Collection for Atomic to Continuum Scales (SCACS)

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Timeline

PostedAug 18

subcontract

Response DeadlineFeb 16

Submission deadline

Response Deadline

02/16/2027, 12:00:00 AM UTCin 5 months

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Organization & Contact Information

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AgencyDepartment Of Energy → Triad - DOE Contractor

ContactsNo contacts available

OfficeN/A

Organization / Agency

Department Of Energy → Triad - DOE Contractor

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Office AddressN/A

ContactsNo contact information available

Full Description

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Performs acceleration and optimization of the SCACS platform for licensees of DOE technology. Implements Graph Neural Networks (GNNs) to accelerate atomic-site-projected conductivity methods, trains models on million-atom microstructure datasets, and optimizes AI inference for physics-grounded predictions. Requires High-Performance Computing (HPC) clusters and GPU acceleration frameworks. Delivers an optimized, accelerated AI simulation engine.

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