An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules

• Published in: Molecules (Basel, Switzerland) Vol. 27; no. 23; p. 8567
• Main Authors: Wang, Yanxing, Walker, Brandon Duane, Liu, Chengwen, Ren, Pengyu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2022; MDPI
• Subjects: Accuracy; AMOEBA force field; Chemical properties; Chemistry; computational efficiency; conformational energy profile; Datasets; Deep learning; Density functionals; Energy; Geometry; Methods; Molecular Conformation; neural network potential; Neural Networks, Computer; Optimization; Physical Phenomena; Quantum Theory; semi-empirical method; Simulation; United States; neural network potential; computational efficiency; AMOEBA force field; conformational energy profile; semi-empirical method
• ISSN: 1420-3049; 1420-3049
• DOI: 10.3390/molecules27238567

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.