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Fenglei Cao

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Artificial Intelligence Machine Learning cond-mat.mtrl-sci physics.chem-ph

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preprint2026arXiv

A Pre-trained Reaction Embedding Descriptor Capturing Bond Transformation Patterns

With the rise of data-driven reaction prediction models, effective reaction descriptors are crucial for bridging the gap between real-world chemistry and digital representations. However, general-purpose, reaction-wise descriptors remain scarce. This study introduces RXNEmb, a novel reaction-level descriptor derived from RXNGraphormer, a model pre-trained to distinguish real reactions from fictitious ones with erroneous bond changes, thereby learning intrinsic bond formation and cleavage patterns. We demonstrate its utility by data-driven re-clustering of the USPTO-50k dataset, yielding a classification that more directly reflects bond-change similarities than rule-based categories. Combined with dimensionality reduction, RXNEmb enables visualization of reaction space diversity. Furthermore, attention weight analysis reveals the model's focus on chemically critical sites, providing mechanistic insight. RXNEmb serves as a powerful, interpretable tool for reaction fingerprinting and analysis, paving the way for more data-centric approaches in reaction analysis and discovery.

preprint2026arXiv

Nested Spatio-Temporal Time Series Forecasting

Spatiotemporal forecasting is critical for real-world applications like traffic management, yet capturing reliable interactions remains challenging under noisy and non-stationary conditions. Existing methods primarily rely on historical spatial priors, often failing to account for evolving temporal correlations and suffering from systematic errors. In this work, we propose a nested forecasting framework that couples future macro-level regional trends with micro-level historical observations, enabling top-down guidance from abstract future representations for fine-grained forecasting. Specifically, we employ a spectral clustering-based approach to construct semantically coherent regions, providing both theoretical and empirical evidence that this representation effectively filters systematic noise while preserving essential trends. Building on this, we develop a progressive coarse-to-fine predictor to integrate these representative features into the inference process. This enables the model to leverage trend predictions to anticipate dynamic anomalies, such as periodic offsets, in advance. Furthermore, extensive experiments on multiple high-dimensional datasets demonstrate that our method consistently outperforms state-of-the-art baselines, validating the effectiveness of future macro-guided nested forecasting.

preprint2011arXiv

The Structural, Energetic and Electronic Properties of Doped Carbon Nanotubes by Encapsulation of MCp2 (M=Fe, Co, Ni): a Theoretical Investigation

Metallocenes can be encapsulated inside the carbon nanotubes. The structural, energetic and electronic properties of organometallic MCp2@SWCNT are obtained from DFT method. We verify that such encapsulation is noncovalent functionalization, and examined binding energies and charge transfers of MCp2@(16,0)SWCNT systems. Consistent with recent experimental findings, the optimal distance between FeCp2 center and near tube-wall is 4.7 (5.1) Å for the configuration where MCp2's five-fold axis is parallel (vertical) to nanotube axis, while the minimal diameter is 9.4 (10.2) Å to exothermically encapsulate FeCp2 molecules. Finally we clarify the doping effects near the bandgap by encapsulations of CoCp2 and NiCp2.