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Abstract With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient's condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models. 

Raman and infrared (IR) spectra provide rich information about materials. In this study, we employ first-principles calculations to predict the temperature-dependent linewidths of zone-center phonon modes, along with the IR dielectric function in bulk hexagonal boron nitride. We include the contributions of three-phonon, four-phonon scattering, and phonon renormalization, and our predictions show good agreement with our own experimental results as well as those in the literature. Our findings show that the temperature dependency of phonon linewidth would be strengthened by considering four-phonon scattering while weakened by further including phonon renormalization. After considering all these effects, four-phonon scattering shows a significant or even leading contribution to the linewidth over three-phonon scattering, especially at elevated temperatures. 

Despite its importance, a sophisticated theoretical study of thermal conductivity in bulk h-BN has been lacking to date. In this study, we predict thermal conductivity in bulk h-BN crystals using first-principles predictions and the Boltzmann transport equation. We consider three-phonon (3ph) scattering, four-phonon (4ph) scattering, and phonon renormalization. Our predicted thermal conductivity is 363 and 4.88 W/(m K) for the in-plane and out-of-plane directions at room temperature, respectively. Further analysis reveals that 4ph scattering reduces thermal conductivity, while phonon renormalization weakens phonon anharmonicity and increases thermal conductivity. Eventually, the in-plane and out-of-plane thermal conductivities show intriguing ∼T−0.627 and ∼T−0.568 dependencies, respectively, far deviating from the traditional 1/T relation. 

Abstract 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 unprecedentedq-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. 

Finite element analysis provides visual insights into conductive path evolution in a SiO₂-based memristor. Electrochemical impedance spectroscopy experimentally validated the theoretical findings by interpreting with an equivalent circuit. 

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.