arXiv:2606. 17476v1 Announce Type: new Abstract: Laser-induced breakdown spectroscopy (LIBS) quantitative analysis faces critical challenges in wavelength selection due to high-dimensional spectral data and the fundamental trade-off between prediction accuracy and feature efficiency.
arXiv:2607. 27645v1 Announce Type: cross Abstract: Wavelength selection is one of the important preprocessing methods in near-infrared spectroscopy to improve prediction accuracy and interpretability of spectral data.
arXiv:2606. 20087v1 Announce Type: new Abstract: Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity.
arXiv:2603. 23101v3 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence.
arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.
arXiv:2606. 10130v1 Announce Type: cross Abstract: Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics.
arXiv:2606. 06765v1 Announce Type: cross Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry.
arXiv:2606. 14874v1 Announce Type: cross Abstract: High-purity germanium gamma spectra often require time-consuming analyses from subject matter experts.
arXiv:2607. 23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations.
arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.
arXiv:2608. 16082v1 Announce Type: new Abstract: Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous.
arXiv:2606. 02662v1 Announce Type: cross Abstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data.
arXiv:2608. 00956v1 Announce Type: new Abstract: This paper presents an interpretable machine-learning framework for predicting the splitting strength (ST) of asphalt concrete and supporting data-driven mixture design.