An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.
An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.
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DOI：
10.3390/molecules27238567
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发表时间：
2022-12-05
期刊：
影响因子：
--
通讯作者：
Ren P
中科院分区：
文献类型：
--
作者：
Wang Y;Walker BD;Liu C;Ren P
准确的分子构象能量对认识分子的化学性质具有重要意义。它们也是力场高质量参数化的基础。传统上，精确的构象剖面是用密度泛函理论（DFT）方法得到的。然而，当分子大小相对较大或有许多感兴趣的分子时，获得可靠的能量分布可能是耗时的。此外，将数据驱动的深度学习方法纳入力场开发对高质量的几何和能量数据有很大的要求。为此，我们比较了几种可能替代传统DFT方法进行构象扫描的方法，包括半经验方法GFN2-xTB和神经网络电位ANI-2x。研究发现，采用半经验方法和单点能量计算方法进行几何构型优化的顺序方案，可以将优化速度提高数百倍，从而获得满意的构象能剖面。
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtained with density functional theory (DFT) methods. However, obtaining a reliable energy profile can be time-consuming when the molecular sizes are relatively large or when there are many molecules of interest. Furthermore, incorporation of data-driven deep learning methods into force field development has great requirements for high-quality geometry and energy data. To this end, we compared several possible alternatives to the traditional DFT methods for conformational scans, including the semi-empirical method GFN2-xTB and the neural network potential ANI-2x. It was found that a sequential protocol of geometry optimization with the semi-empirical method and single-point energy calculation with high-level DFT methods can provide satisfactory conformational energy profiles hundreds of times faster in terms of optimization.
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影响因子：
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作者：
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