ONHS Report: DARPA-PA-25-07-04

Resilient

Solicitation ID: DARPA-PA-25-07-04

Agency: DARPA/DSO

Type: Solicitation

Deadline: 2026-10-19T16:00:00 ET

Eligibility: Maybe (Team Member/MIPR)

Funding: Total award value limited to $2,000,000, with Phase 1 (base) not to exceed $1,000,000 and Phase 2 (option) not to exceed $1,000,000.

Doc Type: BAA

Clearance: CUI

Program Manager: Resilient@darpa.mil

Last Updated: 2026-08-21 16:03

Analyzed File: DARPA-PA-25-07-04.pdf

Eligibility Reasoning

any proposal submitted directly by these entities [FFRDCs] in a prime contractor capacity may be deemed non-conforming and not evaluated. Proposals that include a... FFRDC... as a subcontractor may also be deemed non-conforming unless... cost proposals must exclude their funding, as DARPA will not fund them through the prime.

Note: This is an AI-generated summary...

Summary

The Defense Advanced Research Projects Agency (DARPA) Resilient program seeks to develop an integrated system for accelerated materials testing and service-life prediction of concrete infrastructure. The program aims to overcome the limitations of current testing methods by creating a system that can simulate decades of environmental degradation in a matter of weeks. The core of the program involves the co-development of two components: 1) a portable 'Material Accelerator' platform capable of subjecting concrete coupons to coupled, synergistic stressors (mechanical, chemical, thermal) to achieve over 1,000x acceleration of aging, and 2) a physics-informed, multi-scale 'Scalability Model' to translate the accelerated, micro-scale coupon data into accurate predictions of long-term, macro-scale structural performance. The program is structured into a 12-month base phase targeting >100x acceleration and a 12-month option phase targeting >1,000x acceleration, with a total award value of up to $2,000,000 per performer. A key requirement is for performers to contribute historical concrete degradation data to a government-aggregated federated database that will serve as a shared baseline for model validation across the program.

Overall Technical Areas

Accelerated Materials TestingPhysics-Informed Multiscale ModelingStructural Performance PredictionMaterial Degradation KineticsMicrostructural CharacterizationQuantitative Morphological AnalysisExperimental Platform DesignSynergistic Stressor SimulationChemo-Mechanical Coupling ModelsIn-Situ Non-Destructive EvaluationFederated Data AnalysisSensor Systems & Data AcquisitionScientific Software DevelopmentThermodynamic Similitude Analysis

Probable LBNL Areas

CSAEESAESAPSA

Focus Areas

Example Priorities:

A Scalability Model: This is an advanced, physics-informed, multi-scale modeling framework. Its purpose is to understand the fundamental mechanics of degradation and predict how micro-scale damage will evolve into macro-scale structural failure over time.
Proposers must therefore develop advanced, physics-informed, multi-scale modeling frameworks capable of reconciling these divergent scaling kinetics to predict emergent, macroscopic structural failure modes from accelerated coupon-level microstructural data.
Explain how the model will translate accelerated coupon data into service-life predictions for complex structures.
Proposers must identify the specific, rate-limiting kinetic equations (e.g., modified Arrhenius, Nernst-Planck) they will adapt for non-linear, multi-stressor environments.

Specific Technical Skills:

Multi-scale ModelingComputational MechanicsPhysics-Informed Machine LearningMaterial Degradation KineticsFinite Element AnalysisDamage MechanicsData ScienceUncertainty Quantification

Potential PIs for this Area:

Name	Score	Organization
Philip Mallon	0.74	PSA \| Engineering \| Mechanical Engineering · Material Degradation Kinetics
Michael Mahoney	0.74	CSA \| SciData \| Data Science Research · Data Science
Anna Giannakou	0.74	CSA \| SciData \| Data Science Applications · Data Science
Tarek Zohdi	0.74	PSA \| Engineering \| Engineering Division Office · Computational Mechanics
Zhe Bai	0.74	CSA \| AMCR \| Computer Science · Physics-Informed Machine Learning
Aditi Krishnapriyan	0.73	CSA \| AMCR \| Computer Science · Physics-Informed Machine Learning
Qin Yu	0.73	ESA \| MSD \| Material Physics · Damage Mechanics
Yuanran Zhu	0.73	CSA \| AMCR \| Applied Mathematics · Multi-scale Modeling
Yingqi Zhang	0.72	EESA \| EG \| Hydrogeology Dept · Uncertainty Quantification
Suncica Canic	0.71	CSA \| AMCR \| Mathematics · Finite Element Analysis

Example Priorities:

A Material Accelerator: This is the physical, scalable, testing platform. Guided by the Scalability Model, it subjects concrete coupons to the precise, synergistic, and accelerated stressors required to rapidly generate high-fidelity data.
This sophisticated experimental platform will shift testing away from reductionist methods by subjecting multiple material coupons simultaneously to the precise, coupled, and accelerated stressors that drive real-world degradation.
The ultimate objective is a scalable system that can accurately replicate and accelerate the synergistic complexity of real-world environmental variables across many samples or environments in parallel.
Proposers must describe the specific physical, chemical, or thermodynamic control mechanisms their system will use to achieve >1,000x acceleration without inducing artificial surface blocking or fundamentally altering the diffusion kinetics.

Specific Technical Skills:

Materials ScienceMechanical EngineeringSensor IntegrationData Acquisition SystemsNon-Destructive Evaluation (NDE)Prototyping & FabricationThermal EngineeringCorrosion ScienceChemical Engineering

Potential PIs for this Area:

Name	Score	Organization
Francesco Ricci	0.75	ESA \| MSD \| Material Physics · Materials Science
Kristin Persson	0.75	ESA \| MSD \| Material Physics · Materials Science
Didier Perrodin	0.74	ESA \| MSD \| Material Physics · Prototyping & Fabrication
Neil Razdan	0.74	ESA \| CSD \| Catalysis · Chemical Engineering
Peter Tennessen	0.73	PSA \| Engineering \| Mechanical Engineering · Mechanical Engineering
Niklas Mundhenk	0.72	EESA \| EG \| Geophysics Dept · Corrosion Science
Jeremy Smith	0.72	EESA \| EG \| Geophysics Dept · Thermal Engineering
Dan Gunter	0.72	CSA \| SciData \| Data Science Applications · Data Acquisition Systems
Victor Negut	0.72	PSA \| NSD \| ANP · Sensor Integration
Philip Mallon	0.71	PSA \| Engineering \| Mechanical Engineering · Non-Destructive Evaluation (NDE)

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Nobumichi Tamura	0.78	ESA \| ALS \| ALS Photon Science Operations · Microstructural Characterization
Gregory Lemieux	0.78	EESA \| CESD \| Earth System Sciences · Scientific Software Development
Jeremy Smith	0.77	EESA \| EG \| Geophysics Dept · Chemo-Mechanical Coupling Models
Philip Mallon	0.76	PSA \| Engineering \| Mechanical Engineering · Material Degradation Kinetics
Piotr Zarzycki	0.76	EESA \| EG \| Geochemistry Dept · Structural Performance Prediction
Yuan Mei	0.76	PSA \| PHY \| Atlas · Experimental Platform Design
Andy Nonaka	0.75	CSA \| AMCR \| Applied Mathematics · Physics-Informed Multiscale Modeling
Devarshi Ghoshal	0.75	CSA \| SciData \| Data Science Applications · Federated Data Analysis
Pramod Bhuvankar	0.75	EESA \| EG \| Hydrogeology Dept · Thermodynamic Similitude Analysis
Jiannan Wang	0.75	EESA \| EG \| Geophysics Dept · In-Situ Non-Destructive Evaluation

Fundamental Research Exemption (FRE)

As of the date of publication of this solicitation, the Government expects that program goals as described herein may be met by proposers intending to perform fundamental research and does not anticipate applying publication restrictions of any kind to individual awards for fundamental research that may result from this solicitation.