Assessing conformer energies using electronic structure and machine learning methods
Assessing conformer energies using electronic structure and machine learning methods
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DOI：
10.26434/chemrxiv.11920914.v1
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发表时间：
2020-03
影响因子：
2.2
通讯作者：
Dakota L Folmsbee;G. Hutchison
中科院分区：
文献类型：
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作者：
Dakota L Folmsbee;G. Hutchison
我们对当前的计算方法进行了大规模评估，包括传统的小分子力场、半经验、密度泛函、从头算电子结构方法以及当前的机器学习（ML）技术来评估相对单点能量。使用多达10个局部最小几何形状跨越约700个分子，每个都通过B3 LYP-D3 BJ用单点DLPNO-CCSD（T）三重zeta能量优化，我们考虑了超过6，500个单点来比较不同方法之间的相关性，以获得相对能量和最小值的有序排序。我们从目前的ML方法中找到了希望，并在每一层的准确性-时间权衡中推荐了方法，特别是最近的GFN 2半经验方法，B 97 -3c密度泛函近似和RI-MP2，用于精确的构象能量。ANI系列ML方法显示出了希望，特别是部分基于耦合簇能量训练的ANI-1ccx变体。多种方法表明，在性能和准确性方面都应该继续改进。
We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.