AI 中文总结
该研究发布OpenGEM26数据集，涵盖大量有机分子构象。基于此训练图神经网络势GPTFF-mol，能量平均绝对误差达16 meV/分子，力预测性能优。经测试验证，能准确描述分子动力学行为和反应势垒，为含硫氯有机分子模拟提供有力资源。
AI 中文摘要
密度泛函理论（DFT）是原子分子模拟的可靠工具，机器学习势则是平衡精度和效率的有力补充。本文发布了OpenGEM26数据集，包含20万个独特分子和440万个由H、C、N、O、S和Cl组成的构象，最多含10个重原子。所有计算在特定水平并带色散校正下进行，记录了完整结构优化轨迹和丰富非平衡结构。统计分析表明该数据集在能量、键长和键角方面比QM9覆盖更宽构象空间。基于此训练的图神经网络势GPTFF-mol，能量平均绝对误差为16 meV/分子，力预测性能优于ANI-2x，并通过丁烷旋转和酮-烯醇互变异构测试验证，能准确描述扭曲几何形状下的分子动力学行为和反应势垒。这项工作为含硫和氯有机分子的高效模拟提供了高质量资源和强大的机器学习势。
英文摘要
Density functional theory (DFT) serves as a reliable tool for atomistic molecular simulations, while machine learning potentials have become powerful complements to balance accuracy and efficiency. In this work, we release OpenGEM26 (Open Generated Ensemble of Molecules, 2026), a large-scale dataset comprising 200,000 unique molecules and 4.4 million conformations composed of H, C, N, O, S and Cl with up to ten heavy atoms. All calculations are carried out at the ωB97X-D/Def2-SVP and Def2-TZVP levels with dispersion corrections, and complete structural optimization trajectories and abundant non-equilibrium structures are recorded. Statistical analyses confirm that this dataset covers a broader conformational space than QM9 in terms of energy, bond lengths and bond angles. A graph neural network-based potential GPTFF-mol is trained using the new dataset, achieving an energy mean absolute error of 16 meV/molecule, which is equivalent to 0.82meV/atom, and superior force prediction performance compared with ANI-2x. Validated by butane rotation and keto-enol tautomerization tests, the model accurately describes molecular dynamical behaviors and reaction barriers at distorted geometries. This work provides a high-quality resource and robust ML potential for efficient simulations of sulfur- and chlorine-containing organic molecules.
