ONHS Report: S-196281

Simulator Collection for Atomic to Continuum Scales (SCACS)

Solicitation ID: S-196281

Agency: DOE

Type: Special Notice

Deadline: 2027-02-15T19:00:00 ET

Eligibility: Maybe (Unspecified)

Funding: N/A

Doc Type: Other

Clearance: No

Program Manager: licensing@lanl.gov

Last Updated: 2026-08-19 16:02

Analyzed File: Tech-Snapshot-SCACS-26-25623.pdf

Eligibility Reasoning

Eligibility for FFRDCs or National Labs was not mentioned.

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

Summary

The Simulator Collection for Atomic to Continuum Scales (SCACS) is an AI-driven, multiscale simulation platform developed by Los Alamos National Laboratory to accelerate the design and deployment of advanced materials. It bridges the gap between high-fidelity atomic-scale physics and engineering-scale continuum models by using graph neural networks trained on proprietary methods (SPTC-AI for thermal and SPEC-AI for electronic conductivity). SCACS decomposes a material's conductivity into per-atom contributions, allowing it to predict spatially varying and direction-dependent heat and electricity flow across large, million-atom systems. This enables engineers to identify potential failure points like hot-spots and accurately model the impact of microstructural defects. The platform significantly reduces simulation times from days to minutes and integrates with standard finite element solvers, providing a physics-grounded tool for industries such as semiconductors, fusion energy, and aerospace to improve product reliability and shorten development cycles.

Overall Technical Areas

Multiscale Simulation PlatformsPhysics-Informed Machine LearningGraph Neural NetworksFinite Element AnalysisComputational Materials ScienceThermal Transport ModelingElectrical Transport ModelingAtomistic SimulationsMolecular DynamicsContinuum Mechanics ModelsHigh-Performance ComputingAnisotropic Material PropertiesFirst-Principles CalculationsCoarse-Graining Methods

Probable LBNL Areas

CSAEESAESAPSA

Focus Areas

Example Priorities:

The SCACS Toolkit is an AI-driven multiscale simulation platform designed to accelerate the development and deployment of advanced materials.
Built on novel atomic-site-projected conductivity methods and accelerated through graph neural networks, SCACS predicts spatially varying, direction-dependent thermal and electrical transport...
A machine-learning graph neural network then learns these atomic-scale contributions from a curated training set and scales the predictions up to representative volume elements suitable for finite-element analysis.
SCACS bridges this gap by embedding machine-learned physics directly into engineering-scale simulations.

Specific Technical Skills:

Graph Neural NetworksMachine LearningMultiscale ModelingAtomistic SimulationMolecular DynamicsHigh-Performance ComputingComputational PhysicsModel Acceleration

Potential PIs for this Area:

Name	Score	Organization
Angelos Ioannou	0.75	CSA \| AMCR \| Computer Science · High-Performance Computing
Costin Iancu	0.75	CSA \| AMCR \| Computer Science · High-Performance Computing
Kristin Persson	0.74	ESA \| MSD \| Material Physics · Atomistic Simulation
Nirmalendu Patra	0.74	CSA \| AMCR \| Computer Science · Model Acceleration
Aurelie Champagne	0.74	ESA \| MSD \| Material Physics · Computational Physics
Yuanran Zhu	0.74	CSA \| AMCR \| Applied Mathematics · Multiscale Modeling
Talita Perciano Costa Leite	0.73	CSA \| SciData \| Data Science Research · Machine Learning
Bingqing Cheng	0.73	ESA \| CSD \| Gas Phase · Molecular Dynamics
Michael Mahoney	0.73	CSA \| SciData \| Data Science Research · Machine Learning
Daniel Murnane	0.72	CSA \| SciData \| Data & Computational Science · Graph Neural Networks

Example Priorities:

The Site-Projected Thermal Conductivity (SPTC) and Space-Projected Electronic Conductivity (SPEC) methods decompose a material’s bulk conductivity into per-atom contributions, revealing how individual phases, defects and interfaces locally steer the flow of heat or charge.
Reveals localized hot-spots and transport pathways that conventional continuum models routinely miss.
Captures anisotropy and spatial variation in both thermal and electrical conductivity, giving engineers a directionally accurate picture of material behavior.
By coarse-graining atom-resolved conductivity into spatially varying fields rather than collapsing them to a single bulk value, the technology preserves the heterogeneity, interfaces and defect populations...

Specific Technical Skills:

Materials ScienceThermal ConductivityElectrical ConductivityHeat Transfer SimulationCondensed Matter PhysicsAnisotropy ModelingDefect AnalysisMicrostructure Characterization

Potential PIs for this Area:

Name	Score	Organization
Nobumichi Tamura	0.76	ESA \| ALS \| ALS Photon Science Operations · Microstructure Characterization
Francesco Ricci	0.75	ESA \| MSD \| Material Physics · Materials Science
Kristin Persson	0.75	ESA \| MSD \| Material Physics · Materials Science
Hans Wenk	0.75	ESA \| ALS \| ALS Photon Science Operations · Microstructure Characterization
Zahid Hussain	0.75	ESA \| MSD \| Material Physics · Electrical Conductivity
Ehud Altman	0.74	ESA \| MSD \| Material Physics · Condensed Matter Physics
Koushik Sen	0.73	CSA \| AMCR \| Computer Science · Defect Analysis
Michael Pettes	0.73	ESA \| MF \| MF NCEM Facility · Thermal Conductivity
Jeremy Smith	0.72	EESA \| EG \| Geophysics Dept · Heat Transfer Simulation
Zhi Jackie Yao	0.72	CSA \| AMCR \| Applied Mathematics · Anisotropy Modeling

Example Priorities:

SCACS... translate first-principles insights into spatially resolved transport properties that can be used within standard finite element workflows.
The companion solver modules called sptc2fem and spec2fem, built on FEniCSx, ingests the resulting thermal and electronic conductivity fields...
Easily Integrates with exiting finite-element solvers, such as Abaqus.
Computer-aided engineering software (constitutive model inputs for industry-standard simulation platforms)

Specific Technical Skills:

Finite Element MethodsComputer-Aided Engineering (CAE)Software IntegrationFEniCSxAbaqusConstitutive ModelingScientific ComputingWorkflow Automation

Potential PIs for this Area:

Name	Score	Organization
Jean Sexton	0.74	CSA \| AMCR \| Applied Mathematics · FEniCSx
Michael Mahoney	0.74	CSA \| SciData \| Data Science Research · Scientific Computing
Ann Almgren	0.74	CSA \| AMCR \| Applied Mathematics · Scientific Computing
Anna Giannakou	0.73	CSA \| SciData \| Data Science Applications · Workflow Automation
Per-Olof Persson	0.73	CSA \| AMCR \| Mathematics · Finite Element Methods
Ishan Srivastava	0.73	CSA \| AMCR \| Applied Mathematics · Constitutive Modeling
Kunhwi Kim	0.73	EESA \| EG \| Geophysics Dept · Constitutive Modeling
Gregory Lemieux	0.72	EESA \| CESD \| Earth System Sciences · Software Integration
Tarek Zohdi	0.72	PSA \| Engineering \| Engineering Division Office · Computer-Aided Engineering (CAE)
Jeremy Smith	0.70	EESA \| EG \| Geophysics Dept · Abaqus

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
Francesco Ricci	0.79	ESA \| MSD \| Material Physics · Computational Materials Science
Kristin Persson	0.78	ESA \| MSD \| Material Physics · Atomistic Simulations
Lisa Claus	0.76	CSA \| NERSC \| Sci Eng & Workflows Department · High-Performance Computing
Ben Erichson	0.75	CSA \| SciData \| Data Science Research · Physics-Informed Machine Learning
Zhi Jackie Yao	0.75	CSA \| AMCR \| Applied Mathematics · Electrical Transport Modeling
Piotr Zarzycki	0.75	EESA \| EG \| Geochemistry Dept · Molecular Dynamics
Ahmad Omar	0.75	ESA \| MSD \| Material Physics · Coarse-Graining Methods
Chris Dames	0.74	ETA \| ESDR \| Energy Storage & Dist Dpt · Thermal Transport Modeling
Tarek Zohdi	0.74	PSA \| Engineering \| Engineering Division Office · Continuum Mechanics Models
Daniel Murnane	0.74	CSA \| SciData \| Data & Computational Science · Graph Neural Networks

Fundamental Research Exemption (FRE)

Not Mentioned