arXiv subjects

Shiyi Qin

Publications and source records attributed to Shiyi Qin.

4 recordsLinked to original sources

arXiv2024

Binding memory of liquid molecules

Understanding the binding dynamics of liquid molecules is of fundamental importance in physical and life sciences. However, nanoscale fast dynamics pose great challenges for experimental characterization. Conventionally, the binding dynamics have been assumed to be memoryless. Here, we integrate large scale computer simulation, scaling theory, and real-time single particle tracking microscopy with high spatiotemporal precision to unveil a universal memory effect in the binding dynamics of liquid molecules. This binding memory can be quantified by a binding time autocorrelation function, whose power-law decay depends not only on the binding affinity, but also on the topological and materials properties of the surrounding environment. Context-dependent biomolecular binding memory is likely exploited by biological systems to regulate biochemical reactions and biophysical processes. Deciphering this binding memory offers a novel strategy to probe complex biological systems and advanced soft materials.

physics.chem-ph↗

arXiv2024

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

cs.LG↗

arXiv2022

Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing

We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs can exploit color as a key source of information, which enables the use of modern computer vision hardware (e.g., infrared, thermal, and hyperspectral cameras). We illustrate the concepts using diverse case studies arising in spectral analysis, molecule design, sensor design, image-based control, and multivariate process monitoring.

cs.CV↗

arXiv2015

Three-dimensional graphene skeletons supported nickel molybdate nanowire composite as novel ultralight electrode for supercapacitors

Nickel molybdate (NiMoO$_{4}$) nanowires were prepared on chemical-vapor-deposition-grown three-dimensional graphene skeletons by hydrothermal method. The X-ray diffraction and Raman results show that NiMoO$_{4}$ is $α$ phase. This binder-free and ultralight graphene/ NiMoO$_{4}$ composite was used as a positive electrode for supercapacitors. This electrode presents a high specific capacitance of 1194 F g$^{-1}$ at 12 mA cm$^{-2}$ and the good stability with a cycling efficiency of 97.3% after 1000 cycles. Further, the energy density reaches an energy density of 41 Wh kg$^{-1}$ at a steady power density of 1319 W kg$^{-1}$. These results demonstrate the potential of the designed composite for the future flexible and lightweight energy storage.

cond-mat.mtrl-sci↗