# ACED: A Unified Framework of Physics-informed and Domain-Adapted Generative Diffusion Model for Efficient and Reliable Nanophotonics Inverse Design

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Regents of the University of Michigan - Ann Arbor (MI) · $500,000

## Abstract

Nanophotonics has become of critical importance in advancing the frontiers of modern science and technologies, including integrated photonics for information technologies, photonic quantum information systems, superresolution imaging, sensing etc. In nanophotonics, light is controlled by nanoscale structures engineered precisely for the desired photonic properties. Traditionally designs of specific nanophotonic devices are obtained through empirical, trial-and-error methods with very limited, high level guidance by physics models and intuition. The advancements in artificial intelligence (AI) techniques open up new opportunities to more efficiently design new and more optimal nanophotonic systems. Yet there are many fundamental challenges at present, such as requirement of large training data sets, domain adaptation issues, and limited generalization capabilities. A promising new approach that may mitigate these limitations is to use generative models, particularly score-based diffusion models. This project aims to develop an innovative deep learning framework that combines physics-informed principles with scientific domain-adapted generative diffusion models to overcome key challenges in scientific inverse design and accelerate scientific discovery. The research will advance the frontiers of artificial intelligence and nanophotonics. Furthermore, the developed methods are potentially generalizable to other scientific disciplines. Educational impacts include enhancing enginee

## Key facts

- **NSF award ID:** 2435746
- **Awardee organization:** Regents of the University of Michigan - Ann Arbor (MI)
- **SAM.gov UEI:** GNJ7BBP73WE9
- **PI:** Liyue Shen
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Nanomaterials, LEARNING & INTELLIGENT SYSTEMS
- **Estimated total:** $500,000
- **Funds obligated:** $500,000
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 05/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2435746

## Citation

> US National Science Foundation, Award 2435746, ACED: A Unified Framework of Physics-informed and Domain-Adapted Generative Diffusion Model for Efficient and Reliable Nanophotonics Inverse Design. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2435746. Licensed CC0.

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