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  1. 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 
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    Free, publicly-accessible full text available February 27, 2025
  2. Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular graph or a single 3D structure, existing works often overlook the flexible nature of molecules, which continuously interconvert across conformations via chemical bond rotations and minor vibrational perturbations. To better account for molecular flexibility, some recent works formulate MRL as an ensemble learning problem, focusing on explicitly learning from a set of conformer structures. However, most of these studies have limited datasets, tasks, and models. In this work, we introduce the first MoleculAR Conformer Ensemble Learning (MARCEL) benchmark to thoroughly evaluate the potential of learning on con- former ensembles and suggest promising research directions. MARCEL includes four datasets covering diverse molecule- and reaction-level properties of chemically diverse molecules including organocatalysts and transition-metal catalysts, extending beyond the scope of common GNN benchmarks that are confined to drug-like molecules. In addition, we conduct a comprehensive empirical study, which benchmarks representative 1D, 2D, and 3D MRL models, along with two strategies that explicitly incorporate conformer ensembles into 3D models. Our findings reveal that direct learning from an accessible conformer space can improve performance on a variety of tasks and models. 
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    Free, publicly-accessible full text available May 7, 2025

    We report on the SRG/eROSITA detection of ultra-soft ($kT=47^{+5}_{-5}$ eV) X-ray emission (LX =$2.5^{+0.6}_{-0.5} \times 10^{43}$ erg s−1) from the tidal disruption event (TDE) candidate AT 2022dsb ∼14 d before peak optical brightness. As the optical luminosity increases after the eROSITA detection, then the 0.2–2 keV observed flux decays, decreasing by a factor of ∼39 over the 19 d after the initial X-ray detection. Multi-epoch optical spectroscopic follow-up observations reveal transient broad Balmer emission lines and a broad He ii 4686 Å emission complex with respect to the pre-outburst spectrum. Despite the early drop in the observed X-ray flux, the He ii 4686  Å complex is still detected for ∼40 d after the optical peak, suggesting the persistence of an obscured hard ionizing source in the system. Three outflow signatures are also detected at early times: (i) blueshifted H α emission lines in a pre-peak optical spectrum, (ii) transient radio emission, and (iii) blueshifted Ly α absorption lines. The joint evolution of this early-time X-ray emission, the He ii 4686 Å complex, and these outflow signatures suggests that the X-ray emitting disc (formed promptly in this TDE) is still present after optical peak, but may have been enshrouded by optically thick debris, leading to the X-ray faintness in the months after the disruption. If the observed early-time properties in this TDE are not unique to this system, then other TDEs may also be X-ray bright at early times and become X-ray faint upon being veiled by debris launched shortly after the onset of circularization.

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  4. 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 HfO2:7%Y from the mixed monoclinic (P21/c)+antipolar orthorhombic (Pbca) phase to pure antipolar orthorhombic (Pbca) phase at approximately 6.3 GPa. This transformation is irreversible, meaning that upon release, the material is kinetically trapped in thePbcametastable state at 300 K. Compression also drives polar orthorhombic (Pca21) hafnia into the tetragonal (P42/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 HfO2.

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    Free, publicly-accessible full text available January 30, 2025
  5. Free, publicly-accessible full text available October 2, 2024
  6. Free, publicly-accessible full text available October 1, 2024