Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active Nematics

Deep learning-based optical flow (DLOF) extracts features in adjacent video frames with deep convolutional neural networks. It uses those features to estimate the inter-frame motions of objects at the pixel level. In this article, we evaluate the ability of optical flow to quantify the spontaneous flows of MT-based active nematics under different labeling conditions. We compare DLOF against the commonly used technique, particle imaging velocimetry (PIV). We obtain flow velocity ground truths either by performing semi-automated particle tracking on samples with sparsely labeled filaments, or from passive tracer beads. We find that DLOF produces significantly more accurate velocity fields than PIV for densely labeled samples. We show that the breakdown of PIV arises because the algorithm cannot reliably distinguish contrast variations at high densities, particularly in directions parallel to the nematic director. DLOF overcomes this limitation. For sparsely labeled samples, DLOF and PIV produce results with similar accuracy, but DLOF gives higher-resolution fields. Our work establishes DLOF as a versatile tool for measuring fluid flows in a broad class of active, soft, and biophysical systems.

Solvent-Aware 2D NMR Prediction: Leveraging Multi-Tasking Training and Iterative Self-Training Strategies

Nuclear magnetic resonance (NMR) spectroscopy plays a pivotal role in various scientific fields, offering insights into structural information, electronic properties and dynamic behaviors of molecules. Accurate NMR spectrum prediction efficiently produces candidate molecules, enabling chemists to compare them with actual experimental spectra. This process aids in confirming molecular structures or pinpointing discrepancies, guiding further investigation. Machine Learning (ML) has then emerged as a promising alternative approach for predicting atomic NMR chemical shits of molecules given their structures. Although significant progresses have been made in predicting one-dimensional (1D) NMR, two-dimensional (2D) NMR prediction via ML remains a challenge due to the lack of annotated NMR training datasets. To address this gap, we propose an iterative self-training (IST) approach to train a deep learning model for predicting atomic 2DNMR shifts and assigning peaks in experimental spectra. Our model undergoes an initial pre-training phase employing a Multi-Task Training (MTT) approach, which simultaneously leverages annotated 1D NMR datasets of both $^{1}\text{H}$ and $^{13}\text{C}$ spectra to enhance its understanding of NMR spectra. Subsequently, the pre-trained model is utilized to generate pseudo-annotations for unlabelled 2D NMR spectra, which are subsequently used to refine the 2D NMR prediction model. Our approach iterates between annotated unlabelled 2D NMR data and refining our 2D NMR prediction model until convergence. Finally, our model is able to not only accurately predict 2D NMR but also annotate peaks in experimental 2D NMR spectra. Experimental results show that our model is capable of accurately handling medium-sized and large molecules, including polysaccharides, underscoring its effectiveness.

Asymmetric Contrastive Multimodal Learning for Advancing Chemical Understanding

The versatility of multimodal deep learning holds tremendous promise for advancing scientific research and practical applications. As this field continues to evolve, the collective power of cross-modal analysis promises to drive transformative innovations, leading us to new frontiers in chemical understanding and discovery. Hence, we introduce Asymmetric Contrastive Multimodal Learning (ACML) as a novel approach tailored for molecules, showcasing its potential to advance the field of chemistry. ACML harnesses the power of effective asymmetric contrastive learning to seamlessly transfer information from various chemical modalities to molecular graph representations. By combining pre-trained chemical unimodal encoders and a shallow-designed graph encoder, ACML facilitates the assimilation of coordinated chemical semantics from different modalities, leading to comprehensive representation learning with efficient training. This innovative framework enhances the interpretability of learned representations and bolsters the expressive power of graph neural networks. Through practical tasks such as isomer discrimination and uncovering crucial chemical properties for drug discovery, ACML exhibits its capability to revolutionize chemical research and applications, providing a deeper understanding of chemical semantics of different modalities.