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CONF PASS:从构象搜索中快速重新优化结构的 DFT。

CONFPASS: Fast DFT Re-Optimizations of Structures from Conformation Searches.

机构信息

Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.

出版信息

J Chem Inf Model. 2023 Jul 24;63(14):4364-4375. doi: 10.1021/acs.jcim.3c00649. Epub 2023 Jul 10.

Abstract

CONFPASS (ormer rioritizations and nalyi for DFT re-optimizations) has been developed to extract dihedral angle descriptors from conformational searching outputs, perform clustering, and return a priority list for density functional theory (DFT) re-optimizations. Evaluations were conducted with DFT data of the conformers for 150 structurally diverse molecules, most of which are flexible. CONFPASS gives a confidence estimate that the global minimum structure has been found, and based on our dataset, we can have 90% confidence after optimizing half of the FF structures. Re-optimizing conformers in order of the FF energy often generates duplicate results; using CONFPASS, the duplication rate is reduced by a factor of 2 for the first 30% of the re-optimizations, which include the global minimum structure about 80% of the time.

摘要

CONFPASS(用于重新优化的优先排序和最终选择)旨在从构象搜索结果中提取二面角描述符,进行聚类,并为密度泛函理论(DFT)重新优化提供优先级列表。评估是使用 150 个结构多样的分子的构象的 DFT 数据进行的,其中大多数是灵活的。CONFPASS 给出了一个置信度估计,表明已经找到了全局最小结构,并且根据我们的数据集,在优化一半的 FF 结构后,我们可以有 90%的置信度。按照 FF 能量的顺序重新优化构象通常会产生重复的结果;使用 CONFPASS,在前 30%的重新优化中,重复率降低了 2 倍,其中大约 80%的时间包含全局最小结构。

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