Fourth-Generation Neural Network Potentials for Molecular Chemistry

机器学习势（MLP）已经成为一个重要的工具，用于执行凝聚系统的原子模拟与电子结构方法的精度在一小部分的计算成本。迄今为止，大多数应用已在材料科学中报道，而有机分子主要是在真空中进行基准研究。虽然大多数化学反应发生在液相中，但将MLP应用于溶液中的溶剂化和分子化学仍然非常罕见。除了所涉及的配置空间的复杂性，研究这些系统的一个主要挑战是需要一个高度准确的描述内以及分子间的相互作用，从强共价键通过氢键静电和分散相互作用。一个特别重要的方面是所涉及物种中的电荷分布，这不能被大多数现有的基于局部性质（如依赖于环境的原子能量和电荷）的MLP正确捕获。（4G-HDNNP），其结合了基于系统中全局电荷分布的局部键合和反应性与长程相互作用的精确描述。这种全局描述不仅对于含有离域电子的分子（例如在芳香族基团或共轭π系统中）是必不可少的，而且如果分子电荷发生变化，例如在质子化（去质子化）中，这是有机化学中许多类型反应的关键步骤。所有这些系统原则上都可以通过4G-HDNNPs进行研究，该方法明确考虑了反应、不同官能团和不同总电荷导致的全局电荷分布，使该方法成为分子化学的一种有前途的方法。该项目的目标是探索4G-HDNNP在溶液中分子化学的适用性，重点关注两个主要方面，密度泛函理论（DFT）参考数据的质量和4G-HDNNP方法的推广。高质量的参考数据将通过对交换相关泛函的可靠性进行基准测试，超越广义梯度近似（GGA）到量子蒙特卡罗和耦合簇计算的水平，并包括色散和自相互作用校正（SIC）。4G-HDNNP将通过采用新的描述符类型进行结构区分来扩展，所述描述符类型甚至适用于像锥形交叉点这样的困难情况，并且通过引入电荷约束来扩展，所述电荷约束与SIC和约束DFT计算一起沿着，将允许以一致的方法克服DFT和4G-HDNNP两者中的整数电荷问题。这套新的计算工具将在开源软件RuNNer中实现，并应用于代表性的溶质-溶剂模型系统，涵盖合成有机化学中的重要场景。

Machine learning potentials (MLP) have become an important tool for performing atomistic simulations of condensed systems with the accuracy of electronic structure methods at a small fraction of the computational costs. To date, most applications have been reported in materials science, while organic molecules have been primarily studied for benchmark purposes in vacuum. Although most chemical reactions occur in the liquid phase, applications of MLPs to solvation and molecular chemistry in solution are still very rare. Apart from the complexity of the involved configuration space, a major challenge for studying these systems is the need for a highly accurate description of intra- as well as intermolecular interactions, from strong covalent bonds via hydrogen bonding to electrostatic and dispersion interactions. A particularly crucial aspect is the charge distribution in the involved species, which cannot be captured correctly by most current MLPs based on local properties like environment-dependent atomic energies and charges.Recently, we have developed a fourth-generation high-dimensional neural network potential (4G-HDNNP), which combines the accurate description of local bonding and reactivity with long-range interactions based on the global charge distribution in the system. This global description is not only essential for molecules containing delocalized electrons, e.g. in aromatic groups or conjugated pi-systems, but also if the molecular charge is changing, like in (de)protonation, which is a key step in many types of reactions in organic chemistry. All these systems can in principle be studied by 4G-HDNNPs, which explicitly take into account the global charge distribution resulting from reactions, different functional groups and varying total charges, making this method a promising approach for molecular chemistry. The goal of this project is to explore the applicability of 4G-HDNNPs to molecular chemistry in solution by focusing on two major aspects, the quality of the density functional theory (DFT) reference data and the generalization of the 4G-HDNNP method. High-quality reference data will be obtained by benchmarking the reliability of exchange correlation functionals beyond the Generalized Gradient Approximation (GGA) level to Quantum Monte Carlo and Coupled Cluster calculations, and by including dispersion and self-interaction corrections (SIC). The 4G-HDNNP will be extended by employing new descriptor types for structural discrimination being applicable even to difficult situations like conical intersections and by the introduction of charge constraints, which, along with SIC and constrained DFT calculations, will allow to overcome the integer charge problem in both, DFT and the 4G-HDNNP, in a consistent approach. This new set of computational tools will be implemented in the open-source software RuNNer and applied to representative solute-solvent model systems covering important scenarios in synthetic organic chemistry.