Machine Learning-Guided Development of Advanced Radiative Cooling Coatings
Project Description
Air conditioning accounted for 11% of the total commercial electricity usage and 16% of residential electricity usage, equating to 113.6 million metric tons of carbon dioxide emission in 2019. On the other hand, nationwide there are 14 million households without access to air conditioning. It can be life threatening during extreme heat waves. Radiative cooling is a passive cooling method that can provide cooling by reflecting the solar radiation and emitting infrared heat both to deep space without energy input, hence greatly saves energy, reduces the carbon emission, and cools down the planet. Recently, Prof. Xiulin Ruan and co-workers have invented ultrawhite radiative cooling paints that can cool surfaces below the ambient temperature under direct sunlight, earning a Guinness World Record title of the “World’s Whitest Paint”. In this project, the Gilbreth fellow will work in broad areas of advanced radiative cooling paint and coating technologies, or other relevant topics of mutual interest. The fellow will be co-mentored by Professors Xiulin Ruan and Guang Lin, with their combined expertise on radiative cooling and machine learning. Potential tasks include but are not limited to AI-accelerated multiscale simulations and design of ultrawhite, colored, and/or dynamic radiative cooling materials and structures. The designs can be fabricated, characterized, and tested for validation and demonstration.
Start Date
Spring, summer, or fall 2026
Postdoc Qualifications
The candidate is expected to have earned (or will earn in the near future) a PhD degree in Mechanical Engineering, Materials Science and Engineering, Chemical Engineering, Physics, or other related fields. Relevant previous research experience and scientific publications are desired. The candidate should also have excellent writing and speaking skills.
Co-advisors
Xiulin Ruan, Professor, School of Mechanical Engineering. Email: ruan@purdue.edu, website: https://engineering.purdue.edu/NANOENERGY/
Guang Lin, guanglin@purdue.edu, Moses Cobb Stevens Professor in Mathematical Sciences (primary appointment), Department of Mathematics, Department of Statistics, School of Mechanical Engineering, https://www.math.purdue.edu/~lin491/
Bibliography
Ziqi Guo, Prabudhya Roy Chowdhury, Zherui Han, Yixuan Sun, Dudong Feng, Guang Lin*, and Xiulin Ruan*, "Fast and Accurate Machine Learning Prediction of Phonon Scattering Rates and Lattice Thermal Conductivity," npj Computational Materials 95, 1 (2023).
Daniel Carne, Joseph Peoples, Ziqi Guo, Dudong Feng, Zherui Han, Xiaojie Liu, and Xiulin Ruan*, “FOS: A fully integrated open-source program for Fast Optical Spectrum calculations of nanoparticle media,” Comp. Phys. Commun. 307, 109393 (2025).
Dudong Feng, Andrew S Witty, Fletcher I Birnbaum, Orlando G Rivera Gonzalez, Andrea Felicelli, Won‐June Lee, Emily C Barber, and Xiulin Ruan*, “Self‐Stratifying Colored Radiative Cooling Paints Through Narrow‐Band Color Preservation Scheme,” Adv. Mater. e04382 (2025).
Xiangyu Li, Joseph Peoples, Peiyan Yao, and Xiulin Ruan, "Ultrawhite BaSO4 Paints and Films for Remarkable Daytime Subambient Radiative Cooling," ACS Appl. Mater. Interfaces, 13, 21733-21739 (2021).
Andrea Felicelli, Ioanna Katsamba, Fernando Barrios, Yun Zhang, Ziqi Guo, Joseph Peoples, George Chiu, and Xiulin Ruan, “Thin layer lightweight and ultrawhite hexagonal boron nitride nanoporous paints for daytime radiative cooling,” Cell Reports Physical Science 3, 101058 (2022).