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Predicting chemical reaction yields is pivotal for efficient chemical synthesis, an area that focuses on the creation of novel compounds for diverse uses. Yield prediction demands accurate representations of reactions for forecasting practical transformation rates. Yet, the uncertainty issues broadcasting in real-world situations prohibit current models to excel in this task owing to the high sensitivity of yield activities and the uncertainty in yield measurements. Existing models often utilize single-modal feature representations, such as molecular fingerprints, SMILES sequences, or molecular graphs, which is not sufficient to capture the complex interactions and dynamic behavior of molecules in reactions. In this paper, we present an advanced Uncertainty-Aware Multimodal model (UAM) to tackle these challenges. Our approach seamlessly integrates data sources from multiple modalities by encompassing sequence representations, molecular graphs, and expert-defined chemical reaction features for a comprehensive representation of reactions. Additionally, we address both the model and data-based uncertainty, refining the model’s predictive capability. Extensive experiments on three datasets, including two high throughput experiment (HTE) datasets and one chemist-constructed Amide coupling reaction dataset, demonstrate that UAM outperforms the stateof-the-art methods. The code and used datasets are available at https://github.com/jychen229/Multimodal-reaction-yieldprediction. 

We combine synchrotron-based infrared absorption and Raman scattering spectroscopies with diamond anvil cell techniques and first-principles calculations to explore the properties of hafnia under compression. We find that pressure drives HfO${}_2$:7%Y from the mixed monoclinic ($P{2}_1/c$)$+$antipolar orthorhombic ($\mathit{Pbca}$) phase to pure antipolar orthorhombic ($\mathit{Pbca}$) phase at approximately 6.3 GPa. This transformation is irreversible, meaning that upon release, the material is kinetically trapped in the$\mathit{Pbca}$metastable state at 300 K. Compression also drives polar orthorhombic ($Pca{2}_1$) hafnia into the tetragonal ($P{4}_2/nmc$) phase, although the latter is not metastable upon release. These results are unified by an analysis of the energy landscape. The fact that pressure allows us to stabilize targeted metastable structures with less Y stabilizer is important to preserving the flat phonon band physics of pure HfO${}_2$.

 

For decades, seismic imaging methods have been used to study the critical zone, Earth's thin, life‐supporting skin. The vast majority of critical zone seismic studies use traveltime tomography, which poorly resolves heterogeneity at many scales relevant to near‐surface processes, therefore limiting progress in critical zone science. Full‐waveform tomography can overcome this limitation by leveraging more seismic data and enhancing the resolution of geophysical imaging. In this study, we apply 2D full‐waveform tomography to match the phases of observed seismograms and elucidate previously undetected heterogeneity in the critical zone at a well‐studied catchment in the Laramie Range, Wyoming. In contrast to traveltime tomograms from the same data set, our results show variations in depth to bedrock ranging from 5 to 60 m over lateral scales of just tens of meters and image steep low‐velocity anomalies suggesting hydrologic pathways into the deep critical zone. Our results also show that areas with thick fractured bedrock layers correspond to zones of slightly lower velocities in the deep bedrock, while zones of high bedrock velocity correspond to sharp vertical transitions from bedrock to saprolite. By corroborating these findings with borehole imagery, we hypothesize that lateral changes in bedrock fracture density majorly impact critical zone architecture. Borehole data also show that our full‐waveform tomography results agree significantly better with velocity logs than previously published traveltime tomography models. Full‐waveform tomography thus appears unprecedentedly capable of imaging the spatially complex porosity structure crucial to critical zone hydrology and processes.

 

Although the professional learning opportunities for teachers to introduce computational thinking (CT) into K-12 education are increasing, it remains challenging to support teachers in integrating CT into their everyday classroom practices. In this study, we have identified six elementary teachers who showed evident eagerness or reluctance in a CT integration professional learning experience. We further analyzed the emerging verbal and non-verbal participation patterns of eagerness and reluctance and the challenges teachers have encountered in the professional learning experience. The results shed light on how to better understand and address the challenges in creating sustainable and effective professional learning.