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Leveling Up Upconverting Nanoparticles with Machine Learning.

Ripeng Luo^1,2, Jungmin Hamm¹, Emory M Chan¹

¹The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

Accounts of Chemical Research

|May 26, 2026

Summary

This summary is machine-generated.

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Artificial intelligence and machine learning accelerate the discovery of brighter, more tunable upconverting nanoparticles (UCNPs). These AI/ML approaches overcome limitations in UCNP brightness and spectral tuning for advanced photonic applications.

Area of Science:

Materials Science
Nanotechnology
Photonic Materials

Background:

Upconverting nanoparticles (UCNPs) convert low-energy light to higher-energy photons, enabling diverse applications.
Current UCNPs suffer from low brightness and limited spectral tunability, hindering widespread adoption.
Optimizing UCNP composition and structure is challenging due to complex photophysics and vast parameter spaces.

Purpose of the Study:

To explore the use of artificial intelligence (AI) and machine learning (ML) to enhance UCNP properties.
To demonstrate AI/ML-driven design of novel UCNP compositions and heterostructures.
To accelerate the discovery of advanced upconverting nanomaterials.

Main Methods:

Utilized automated experimental workflows, physical modeling, and robotic synthesis.

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Integrated ML for data analysis, including image processing and luminescence curve analysis.

Employed closed-loop active learning with Bayesian optimization and kinetic Monte Carlo simulations.

Leveraged differentiable deep learning surrogate models (heterogeneous graph neural networks) for inverse design of UCNP heterostructures.

Main Results:

Achieved a 110-fold enhancement in UCNP emission using active learning.
Identified optimal UCNP core-shell heterostructures through AI-driven design.
Predicted UCNP compositions with 6.5-fold greater emission intensity than the brightest training set UCNP using heterogeneous graph neural networks.
Demonstrated AI's capability to navigate complex compositional spaces and reveal optical phenomena.

Conclusions:

AI and ML are powerful tools for overcoming UCNP limitations in brightness and spectral tunability.
AI/ML approaches significantly accelerate the discovery and optimization of UCNPs.
Future UCNP research will likely involve autonomous self-driving laboratories and large language models for accelerated discovery and fundamental understanding.