Lazzari Federico, Mendolicchio Marco, Barone Vincenzo
Scuola Normale Superiore di Pisa, Piazza dei Cavalieri 7, 56126 Pisa, Italy.
INSTM, via G. Giusti 9, 50121 Firenze, Italy.
J Phys Chem A. 2024 Feb 22;128(7):1385-1395. doi: 10.1021/acs.jpca.3c08382. Epub 2024 Feb 12.
An effective yet reliable computational workflow is proposed, which permits the computation of accurate geometrical structures for large flexible molecules at an affordable cost thanks to the integration of machine learning tools and DFT models together with reduced scaling computations of vibrational averaging effects. After validation of the different components of the overall strategy, a panel of molecules of biological interest have been analyzed. The results confirm that very accurate geometrical parameters can be obtained at reasonable cost for molecules including up to about 50 atoms, which are the largest ones for which comparison with high-resolution rotational spectra is possible. Since the whole computational workflow can be followed employing standard electronic structure codes, accurate results for large-sized molecules can be obtained at DFT cost also by nonspecialists.
本文提出了一种有效且可靠的计算工作流程,由于机器学习工具和密度泛函理论(DFT)模型的集成,以及振动平均效应的缩减标度计算,该流程能够以可承受的成本计算大型柔性分子的精确几何结构。在对整体策略的不同组成部分进行验证之后,对一组具有生物学意义的分子进行了分析。结果证实,对于包含约50个原子的分子,可以以合理的成本获得非常精确的几何参数,这些分子是能够与高分辨率转动光谱进行比较的最大分子。由于整个计算工作流程可以使用标准电子结构代码进行,非专业人员也可以以DFT成本获得大型分子的精确结果。