ONHS Report: FA955027S0002

AFRL/AFOSR-Taiwan NSTC Frontiers in Materials and Nanostructures: AI-Driven Discovery, Quantum Geometry, and Energy-Efficient Information Technologies 2027-2030

Solicitation ID: FA955027S0002

Agency: AFRL/AFOSR

Type: Grant

Deadline: 2027-02-19T00:00:00 ET

Eligibility: Consult DOE Site Office (Prime Status)

Funding: Total available funding of $1,200,000 per year for three years, with individual awards for US PIs up to $150,000 annually.

Doc Type: BAA

Clearance: CUI

Program Manager: alexander.kamrud.1@us.af.mil

Last Updated: 2026-10-28 16:03

Analyzed File: FA955027S0002.pdf

Eligibility Reasoning

None of the following entity types are eligible to submit proposals as primary award recipients under this announcement. • Federally Funded Research and Development Centers (FFRDCs)

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

Summary

This Notice of Funding Opportunity (NOFO) from the Air Force Office of Scientific Research (AFOSR) and Taiwan’s National Science and Technology Council (NSTC) announces a three-year collaborative research program titled "Frontiers in Materials and Nanostructures: AI-Driven Discovery, Quantum Geometry, and Energy-Efficient Information Technologies." The program funds joint basic research projects between U.S. and Taiwanese investigators. Proposals must address one of three core areas: 1) AI-driven materials discovery integrating machine learning with physics-based modeling and experiments; 2) Materials and nanostructures for energy-efficient electronic, photonic, and quantum information technologies; or 3) The study of quantum geometry in solids, including advanced experimental and theoretical methods. Applications require a team of one U.S. and one Taiwanese PI who must first submit a joint pre-proposal. If selected, full proposals are submitted to their respective agencies. Selected U.S. PIs are eligible for up to $150,000 per year for three years, with projects starting around October 2027.

Overall Technical Areas

AI-Driven Materials DiscoveryPhysics-Informed AIQuantum-Level ModelingPredictive Materials DesignAI-Enabled Multiscale ModelingEnergy-Efficient ElectronicsNanoscale Photonic StructuresQuantum Information TechnologiesLow-Dimensional HeterostructuresQuantum Geometry in SolidsTopological MaterialsNonlinear Optical ResponsesMany-Body Systems Theory

Probable LBNL Areas

BSACSAEESAESAETAPSA· Berry Curvature· Materials Synthesis

Focus Areas

Example Priorities:

We seek fundamental studies that integrate artificial intelligence, physics-based modeling, and experimental feedback to accelerate the discovery, synthesis, and understanding of solid-state materials, nanostructures, interfaces, molecules, and hybrid systems.
Proposals may integrate machine learning with ab initio electronic-structure methods, quantum chemistry, molecular dynamics, many-body theory, or multiscale simulations to improve the accuracy, transferability, and efficiency of materials prediction.
Proposals may develop AI-guided approaches to identify stable or metastable materials, molecules, nanostructures, defects, interfaces, and heterostructures with targeted properties.
Proposals may leverage AI to bridge atomistic, mesoscale, and continuum descriptions of heterogeneous, disordered, interfacial, or nonequilibrium systems...

Specific Technical Skills:

Artificial IntelligencePhysics-Informed Machine LearningQuantum ChemistryMolecular DynamicsAb Initio MethodsMultiscale ModelingMaterials Inverse DesignUncertainty QuantificationSynthesis-Aware DesignClosed-Loop Optimization

Potential PIs for this Area:

Name	Score	Organization
Francesco Ricci	0.78	ESA \| MSD \| Material Physics · Ab Initio Methods
Kristin Persson	0.77	ESA \| MSD \| Material Physics · Materials Inverse Design
Yizhi Shen	0.76	CSA \| AMCR \| Applied Mathematics · Quantum Chemistry
Piotr Zarzycki	0.76	EESA \| EG \| Geochemistry Dept · Molecular Dynamics
Ben Erichson	0.75	CSA \| SciData \| Data Science Research · Physics-Informed Machine Learning
Kevin Cruse	0.74	ESA \| MSD \| Material Physics · Synthesis-Aware Design
Andy Nonaka	0.74	CSA \| AMCR \| Applied Mathematics · Uncertainty Quantification
Mohammad Kaazem Pur Mofrad	0.74	BSA \| Molecular Biophysics & Integrated Bioimaging \| Structural Biology Dept · Multiscale Modeling
Tanny Andrea Chavez Esparza	0.74	ESA \| ALS \| PS Computing staff · Artificial Intelligence
Kameshwar Poolla	0.74	ETA \| Energy Technologies and Systems \| Energy Storage & Dist Dpt · Closed-Loop Optimization

Example Priorities:

We seek innovative materials and nanostructures that enable high-performance, low-power operation across computation, memory, data storage, optical interconnects, photonic or optoelectronic information processing, and emerging quantum information functionalities.
...this call targets the synthesis, deterministic manipulation, and microscopic characterization of novel material phases, low-dimensional heterostructures, functional interfaces, and nanoscale photonic structures engineered to approach fundamental limits of energy-efficient information processing.
Emphasis should be placed on material mechanisms, interfacial phenomena, nanoscale control, and experimentally measurable physical observables relevant to energy-efficient information processing.

Specific Technical Skills:

NanostructuresLow-Power ElectronicsPhotonic DevicesOptoelectronicsQuantum Information ProcessingMaterials SynthesisMicroscopic CharacterizationLow-Dimensional HeterostructuresSustainable ComputingInterfacial Phenomena

Potential PIs for this Area:

Name	Score	Organization
Michael Crommie	0.78	ESA \| MSD \| Material Physics · Low-Dimensional Heterostructures
Lane Martin	0.77	· Materials Synthesis
Joelle Frechette	0.77	ETA \| Energy Technologies and Systems \| Energy Storage & Dist Dpt · Interfacial Phenomena
Tara Mishra	0.76	ESA \| MF \| MF NCEM Facility · Microscopic Characterization
Mengjie Yu	0.76	ESA \| MSD \| Material Physics · Photonic Devices
Kin Yu	0.76	ESA \| MSD \| Material Physics · Optoelectronics
Armand Alivisatos	0.75	ESA \| MSD \| Material Physics · Nanostructures
Michael Zaletel	0.75	ESA \| MSD \| Material Physics · Quantum Information Processing
Daniel Gerber	0.75	ETA \| Building & Industrial Energy Systems \| Bldg Technologies Dept · Low-Power Electronics
Gregory Lemieux	0.74	EESA \| CESD \| Earth System Sciences · Sustainable Computing

Example Priorities:

Quantum geometry describes how electronic wavefunctions evolve in momentum space and governs emergent properties of solids, including topological and anomalous transport, nonlinear optical responses, and flat-band superconductivity.
We seek new experimental methodologies to resolve quantum geometry in crystalline solids.
We seek new theoretical frameworks in which quantum geometry serves as a central descriptor of solids, complementing conventional band dispersion, symmetry, and Fermi-surface-based descriptions.
They should clarify how quantum geometry governs nonlinear optical responses, transport, superconductivity, and collective phases, while providing experimentally testable criteria for separating intrinsic quantum-geometric effects from extrinsic contributions.

Specific Technical Skills:

Quantum GeometryTopological MaterialsNonlinear Optical ResponsesFlat-Band SuperconductivityMoiré HeterostructuresBerry CurvatureQuantum MetricFirst-Principles CalculationsMany-Body TheoryGreen's Function Methods

Potential PIs for this Area:

Name	Score	Organization
Feng Wang	0.79	ESA \| MSD \| Material Physics · Moiré Heterostructures
Francesco Ricci	0.77	ESA \| MSD \| Material Physics · First-Principles Calculations
Ehud Altman	0.77	ESA \| MSD \| Material Physics · Many-Body Theory
Marc Vila Tusell	0.77	ESA \| MSD \| Material Physics · Topological Materials
Ran Cheng	0.76	· Berry Curvature
Dunghai Lee	0.76	ESA \| MSD \| Material Physics · Flat-Band Superconductivity
Michael Lindsey	0.75	CSA \| AMCR \| Mathematics · Quantum Metric
Jack Deslippe	0.74	CSA \| NERSC \| Sci Eng & Workflows Department · Green's Function Methods
Mengjie Yu	0.74	ESA \| MSD \| Material Physics · Nonlinear Optical Responses
Yasunori Nomura	0.73	PSA \| PHY \| Theory Group · Quantum Geometry

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 · AI-Driven Materials Discovery
Michael Crommie	0.78	ESA \| MSD \| Material Physics · Low-Dimensional Heterostructures
Ehud Altman	0.77	ESA \| MSD \| Material Physics · Many-Body Systems Theory
Marc Vila Tusell	0.77	ESA \| MSD \| Material Physics · Topological Materials
Daniel Gerber	0.76	ETA \| Building & Industrial Energy Systems \| Bldg Technologies Dept · Energy-Efficient Electronics
Ran Cheng	0.76	· Quantum Geometry in Solids
Zhe Bai	0.76	CSA \| AMCR \| Computer Science · Physics-Informed AI
Anthony Ciavarella	0.76	CSA \| AMCR \| Computer Science · Quantum-Level Modeling
Jeremy Munday	0.75	PSA \| ATAP \| FS&IBT · Nanoscale Photonic Structures
Erhan Saglamyurek	0.75	CSA \| ESNET \| Tech Adv and Engagement · Quantum Information Technologies

Fundamental Research Exemption (FRE)

The Air Force Office of Scientific Research (AFOSR) anticipates that the majority of grants and cooperative agreements awarded under this Broad Agency Announcement (BAA) will involve fundamental research.