Xiulin Ruan

Thermochromic coatings enable passive building energy management, but approaches lack broad tuning ranges, continuous tunability, practical transition temperatures, or scalability. This work introduces a dual-layer thermochromic coating (DLTC) that functionally decouples the black and white states between layers, achieving a transition temperature of 25 °C, solar reflectance tunability of 36.7% (51.4–88.1%), and visible reflectance tunability of 81.2% (6.0–87.2%). DLTC integrates a thin (∼68 μm) black-to-translucent topcoat on an opaque ultrawhite basecoat (>400 μm). Under cold conditions, the topcoat darkens, absorbing solar radiation; as the temperature rises, the topcoat becomes translucent, exposing the ultrawhite basecoat and producing a radiative cooling surface. Outdoor tests show up to 23 °C cooling relative to a black roof in hot weather and 12 °C warming relative to a white roof in cold weather. EnergyPlus simulations predict HVAC energy savings over traditional gray and white cool roofs across U.S. climate zones.

Radiative cooling technologies help alleviate global warming and the urban heat island effect by reducing the energy load of conventional air conditioning. However, most radiative cooling materials contain nonbiodegradable components that decompose into microplastics and threaten the environment. To address this issue, this work develops a biodegradable radiative cooling paint, experimentally demonstrated to maintain an average surface temperature of 2–3 °C below ambient temperature and around 5 °C below commercial white paint under high solar irradiation (>800 W m–2). Fabricated samples showed a high solar reflectance of 96.6% and sky window emissivity of 0.938. The binder demonstrated complete biodegradation within 3 months in a compost environment. TRNSYS simulation shows high energy savings across the US, with over 400 kWh of electricity saved in hot and dry climates such as Phoenix, AZ. With high UV resistance, mechanical durability, and self-cleaning capabilities, the biodegradable radiative cooling paint is suitable for large-scale deployment.

Colored radiative cooling paints offer immense potential for aesthetic thermal management. However, previous studies have been limited to either intuition-driven forward designs that are too expensive to yield non-intuitive optimal designs or to optimizations only at the level of idealized optical spectra. We present a machine-learning-enabled inverse design framework for generating paint formulations with optimal cooling performance and desired color. By integration of photon Monte Carlo simulations with surrogate modeling, this approach bridges the gap between theoretical spectra and physical realization. Our approach uncovers a non-intuitive "double narrow-band absorption" strategy that reduces solar heating power by up to 193 and 37 W/m² compared to conventional additive and single-band subtractive strategies, respectively. A systematic exploration of the HSL color space reveals that 24% of colors can achieve daytime subambient cooling when realistic material limitations are considered. This automated pipeline provides practical guidelines for designing high-performance, aesthetically tailored radiative cooling coatings.

Electrochromic (EC) windows face trade-offs in cost, switching speed, color neutrality, and durability. We present solution-processed n-doped poly(benzodifurandione) (n-PBDF) as a robust organic conductor for black EC windows overcoming these limitations. Utilizing an engineered solvent ink and ultrasonic spray coating, we achieve uniform large-area n-PBDF deposition under ambient conditions without additives. n-PBDF EC electrodes show unprecedented weathering durability (maintaining performance under simultaneous exposure to light (including UV), heat, and humidity for >768 h), addressing a key barrier for organic EC materials. The electrodes exhibit deep black coloration with color neutrality, rapid switching (20,000 cycles). Large-area EC devices demonstrate uniform switching performance, confirming scalable fabrication. Building energy simulations of the EC window reveal significant HVAC savings potential across diverse transitional climates. This work establishes n-PBDF as a scalable, high-performance alternative to conventional inorganic EC systems, advancing the viability of solution-processable smart windows for sustainable architecture.

Heat conduction and radiation are two of the three fundamental modes of heat transfer, playing a critical role in a wide range of scientific and engineering applications ranging from energy systems to materials science. However, traditional physics-based simulation methods for modeling these processes often suffer from prohibitive computational costs. In recent years, the rapid advancements in Artificial Intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in the modeling of nanoscale heat conduction and radiation. This review presents a comprehensive overview of recent AI-driven developments in modeling heat conduction and radiation at the nanoscale. We first discuss the ML techniques for predicting phonon properties, including phonon dispersion and scattering rates, which are foundational for determining material thermal properties. Next, we explore the role of machine-learning interatomic potentials (MLIPs) in molecular dynamics simulations and their applications to bulk materials, low-dimensional systems, and interfacial transport. We then review the ML approaches for solving radiative heat transfer problems, focusing on data-driven solutions to Maxwell's equations and the radiative transfer equation. We further discuss the ML-accelerated inverse design of radiative energy devices, including optimization-based and generative model-based methods. Finally, we discuss open challenges and future directions, including data availability, model generalization, uncertainty quantification, and interpretability. Through this survey, we aim to provide a foundational understanding of how AI techniques are reshaping thermal science and guiding future research in nanoscale heat transfer.

Accurately predicting phonon scattering is crucial for understanding thermal transport properties. However, the computational cost of such calculations, especially for four-phonon scattering, can often be more prohibitive when large number of phonon branches and scattering processes are involved. In this work, we present FourPhonon_GPU, a GPU-accelerated framework for three-phonon and four-phonon scattering rate calculations based on the FourPhonon package. By leveraging OpenACC and adopting a heterogeneous CPU-GPU computing strategy, we efficiently offload massive, parallelizable tasks to the GPU while using the CPU for process enumeration and control-heavy operations. Our approach achieves over 25x acceleration for the scattering rate computation step and over 10x total runtime speedup without sacrificing accuracy. Benchmarking on various GPU architectures confirms the method's scalability and highlights the importance of aligning parallelization strategies with hardware capabilities. This work provides an efficient and accurate computational tool for phonon transport modeling and opens pathways for accelerated materials discovery.

Monte Carlo simulations are commonly used to calculate photon reflectance, absorptance, and transmittance of multi-layer scattering and absorbing media, but they can quickly become prohibitively expensive as the number of layers increases. In this study, we show that although a plain neural network suffers from the curse of dimensionality and fails to yield acceptable predictions of multilayer media, we introduce a recurrent neural network (RNN) trained on the same Monte Carlo simulation dataset to achieve accurate prediction with great acceleration. Our RNN architecture solves the curse of dimensionality by keeping the number of inputs into the network constant for any number of layers. We demonstrate the general applicability with three diverse case studies of multilayer architectures: tissue, radiative cooling paint, and atmospheric clouds, achieving 1-2 orders of magnitude acceleration over Monte Carlo simulations while providing up to one order of magnitude less error than a plain neural network. This recurrent neural network approach enables affordable photon multi-layer modeling, optimization, and high throughput screening for broad applications across dosimetry, atmospheric studies, and spectrally selective radiative coatings.