Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany.
J Phys Chem A. 2022 Jun 9;126(22):3521-3535. doi: 10.1021/acs.jpca.2c02439. Epub 2022 May 26.
We present the first benchmark set focusing on the conformational energies of highly flexible, long -alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g., complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced rSCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and rSCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wave function methods, where MP2/CBS surprisingly performs worse compared to the simple D4 dispersion corrected Hartree-Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parametrization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods.
我们提出了第一个专注于高度灵活的长链烷烃构象能的基准集，称为 ACONFL。无支链烷烃是自然界中无处不在的构建块，因此目标是能够最准确地计算它们的性质，从而改进例如复杂（生物）系统的建模。我们提供了非常准确的 DLPNO-CCSD(T1)/CBS 参考值，这使得即使是最好的可用密度泛函方法也可以进行有意义的统计评估。全面评估了成熟和现代（色散校正）密度泛函的性能。最近引入的 rSCAN-V 函数表现出优异的性能，类似于高效的复合 DFT 方法，如 B97-3c 和 rSCAN-3c，它们提供了更好的成本准确性比，同时几乎达到了具有更大 QZ AO 基组的更计算密集型混合或双混合泛函的准确性。此外，我们研究了常见波函数方法的性能，其中 MP2/CBS 与简单的 D4 色散校正 Hartree-Fock 相比，性能出人意料地更差。此外，我们研究了几种半经验和力场方法的性能，这些方法通常用于在多层次工作流程或大规模分子动力学研究中生成构象系综。最近引入的通用力场 GFN-FF 表现出出色的性能，而其他常用的方法，如通用力场，会产生较大的误差。我们建议将 ACONFL 作为新的半经验或力场方法和机器学习势的参数化以及新开发的 DFT 或色散方法的有意义验证集的有用基准集。
