The prediction of thermal conductivity and radiative properties is crucial. However, computing phonon scattering, especially for four-phonon scattering, could be prohibitively expensive, and the thermal conductivity for silicon after considering four-phonon scattering is significantly under-predicted and not converged in the literature. Here we propose a method to estimate scattering rates from a small sample of scattering processes using maximum likelihood estimation. The calculation of scattering rates and associated thermal conductivity and radiative properties are dramatically accelerated by three to four orders of magnitude. This allows us to use an unprecedented
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Abstract q -mesh (discretized grid in the reciprocal space) of 32 × 32 × 32 for calculating four-phonon scattering of silicon and achieve a converged thermal conductivity value that agrees much better with experiments. The accuracy and efficiency of our approach make it ideal for the high-throughput screening of materials for thermal and optical applications.Free, publicly-accessible full text available February 7, 2025 -
Abstract Many materials have been explored for the purpose of creating structures with high radiative cooling potential, such as nanocellulose-based structures and nanoparticle-based coatings, which have been reported with environmentally friendly attributes and high solar reflectance in current literature. They each have their own advantages and disadvantages in practice. It is worth noting that nanocellulose-based structures have an absorption peak in the UV wavelengths, which results in a lower total solar reflectance and, consequently, reduce radiative cooling capabilities. However, the interwoven-fiber structure of cellulose gives high mechanical strength, which promotes its application in different scenarios. The application of nanoplatelet-based coatings is limited due to the need for high volume of nanoparticles to reach their signature high solar reflectance. This requirement weakens the polymer matrix and results in more brittle structures. This work proposes a dual-layer system, comprising of a cellulose-based substrate as the bottom layer and a thin nanoparticle-based radiative cooling paint as the top layer, where both radiative cooling potential and mechanical strength can be maximized. Experimental and theoretical studies are conducted to investigate the relationship between thickness and reflectance in the top coating layer with a consistent thickness of the bottom layer. The saturation point is identified in this relationship and used to determine the optimal thickness for the top-layer to maximize material use efficiency. With the use of cotton paper painted with a 125 μm BaSO4-based layer, the cooling performance is enhanced to be 149.6 W/m2achieved by the improved total solar reflectance from 80 % to 93 %.
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Abstract Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.
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Abstract Monte Carlo simulations for photon transport are commonly used to predict the spectral response, including reflectance, absorptance, and transmittance in nanoparticle laden media, while the computational cost could be high. In this study, we demonstrate a general purpose fully connected neural network approach, trained with Monte Carlo simulations, to accurately predict the spectral response while dramatically accelerating the computational speed. Monte Carlo simulations are first used to generate a training set with a wide range of optical properties covering dielectrics, semiconductors, and metals. Each input is normalized, with the scattering and absorption coefficients normalized on a logarithmic scale to accelerate the training process and reduce error. A deep neural network with ReLU activation is trained on this dataset with the optical properties and medium thickness as the inputs, and diffuse reflectance, absorptance, and transmittance as the outputs. The neural network is validated on a validation set with randomized optical properties, as well as nanoparticle medium examples including barium sulfate, aluminum, and silicon. The error in the spectral response predictions is within 1% which is sufficient for many applications, while the speedup is 1–3 orders of magnitude. This machine learning accelerated approach can allow for high throughput screening, optimization, or real-time monitoring of nanoparticle media's spectral response.
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Free, publicly-accessible full text available February 1, 2026
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Free, publicly-accessible full text available October 1, 2025
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Free, publicly-accessible full text available June 1, 2025
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Free, publicly-accessible full text available May 1, 2025