CHAOS -- 一种用于σ-轮廓和其他分子描述符的大规模数据库
CHAOS -- A Consistent Large-scale Database for Sigma-Profiles and Other Molecular Descriptors
AI总结：
本文提出CHAOS数据库，提供53091种分子的σ轮廓及其他量子化学属性，显著扩展了公开可用分子数量，并为分子和热力学性质建模提供一致基础。
摘要：
从量子化学计算获得的σ-轮廓是选择溶剂、热力学建模和数据驱动分子设计的关键分子描述符。然而，现有σ-轮廓库在规模和质量上有限，限制了其应用。本文介绍CHAOS（计算高精度观测值和σ-轮廓），提供53091种分子的σ-轮廓及其他量子化学属性，包括气相几何结构、单点导电极化连续（C-PCM）数据、红外光谱、理想气体热容和熵、以及原子轨道核磁共振（NMR）屏蔽张量。所有数据均基于wB97X-D/def2-TZVP理论水平的标准化量子化学工作流程生成。CHAOS数据库涵盖元素多样、分子质量最高达400 amu、偶极矩最高达15 D的分子，并在Zenodo上以开放许可免费提供。它将公开可用σ-轮廓分子数量扩展了数量级，并系统地将其与其他量子化学分子描述符联系起来。CHAOS为化学、化学工程和材料科学中分子和热力学性质模型的发展提供了全面且一致的基础，大大扩展了可能性和可用的量子化学数据基础。
英文摘要：
Sigma-profiles obtained from quantum-chemical calculations are key molecular descriptors for solvent selection, thermodynamic modeling, and data-driven molecular design. However, existing sigma-profile libraries are limited in size and inconsistent in quality, which restricts their utility. In this work, we introduce CHAOS (Computed High-Accuracy Observables and Sigma Profiles), a large-scale and internally consistent database providing sigma-profiles for 53091 molecules, along with additional quantum-chemical observables including gas-phase geometries, single-point conductor-like polarizable continuum (C-PCM) data, infrared spectra, ideal-gas heat capacities and entropies, and atomic orbital nuclear magnetic resonance (NMR) shielding tensors. All data were generated using a standardized quantum-chemical workflow based on an wB97X-D/def2-TZVP level of theory. The CHAOS database covers molecules composed of a diverse set of elements, with molecular masses up to 400 amu and dipole moments up to 15 D, and is freely available on Zenodo under an open license. It extends the number of molecules for which sigma-profiles are publicly available by more than an order of magnitude and systematically links them to a broad range of other quantum-chemical molecular descriptors. CHAOS provides a comprehensive and consistent foundation for developing models of molecular and thermodynamic properties -- both physics-based and machine-learning approaches -- across chemistry, chemical engineering, and materials science, greatly extending the possibilities and the available quantum-chemical data basis.
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