Cooling Greenhouses with AI-Designed Materials: From Simulation to Prototype

The Problem: Heat Stress in Controlled Agriculture

Modern greenhouse agriculture depends on precise climate control — but in high-irradiance regions, cooling costs can account for up to 40% of operational energy expenditure. Passive thermal management through selectively reflective or thermochromic covering materials could dramatically reduce this burden.

The AI Approach

Our ThermoShield project treats material design as a constrained generative optimization problem. We first build a physics-informed surrogate model that maps material composition and microstructure parameters to optical and thermal properties. This surrogate is trained on a combination of first-principles simulations and experimental measurements.

Generative Search

With a fast surrogate in hand, we run a multi-objective generative search that jointly optimizes solar reflectance in the UV-VIS range, transmission in the photosynthetically active radiation (PAR) band, and thermal emissivity. Candidate compositions are ranked and a diverse subset is selected for experimental synthesis and testing.

Prototype Results

Our first prototype composite — a multilayer polymer film with embedded scattering particles — achieved a measured 14°C reduction in peak internal air temperature in a small-scale greenhouse trial compared to standard polyethylene film, without reducing PAR transmission.

Next Steps

We are scaling up synthesis to full greenhouse panels and initiating a field trial with an agricultural partner. Simultaneously, we are extending the surrogate model to cover bio-degradable material candidates.