Fourth-Generation Neural Network Potentials for Molecular Chemistry

第四代神经网络在分子化学方面的潜力

• 批准号: 495842446
• 负责人: Professor Dr. Jörg Behler
• 金额: --
• 依托单位: Department of Physics
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/495842446?language=en
• 关键词: Fourth; Generation; Neural; Network; Potentials

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.

机器学习势 (MLP) 已成为对凝聚系统进行原子模拟的重要工具，其精度与电子结构方法相当，而计算成本仅为一小部分。迄今为止，大多数应用已在材料科学中报道，而有机分子主要在真空中研究用于基准目的。尽管大多数化学反应发生在液相中，但 MLP 在溶液中的溶剂化和分子化学中的应用仍然非常罕见。除了所涉及的构型空间的复杂性之外，研究这些系统的一个主要挑战是需要高度准确地描述分子内和分子间相互作用，从通过氢键的强共价键到静电和色散相互作用。一个特别重要的方面是所涉及物种的电荷分布，目前大多数基于环境依赖性原子能和电荷等局部特性的MLP无法正确捕获电荷分布。最近，我们开发了第四代高维神经网络势（4G-HDNNP），它将局部键合和反应性的准确描述与基于系统中全局电荷分布的长程相互作用结合起来。这种全局描述不仅对于含有离域电子的分子至关重要，例如在芳香族基团或共轭 pi 系统中，而且分子电荷正在变化，例如（去）质子化，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-HDNNP 进行研究，4G-HDNNP 明确考虑了反应产生的全局电荷分布、不同的官能团和变化的总电荷，使该方法成为分子化学的一种有前景的方法。该项目的目标是通过关注密度泛函理论（DFT）参考数据的质量和4G-HDNNP方法的推广两个主要方面来探索4G-HDNNP在溶液中分子化学的适用性。通过对超越广义梯度近似 (GGA) 级别的交换相关函数的可靠性进行基准测试，到量子蒙特卡罗和耦合簇计算，并通过包含色散和自相互作用校正 (SIC)，可以获得高质量的参考数据。 4G-HDNNP 将通过采用新的描述符类型进行结构区分，甚至适用于锥形交叉点等困难情况，并引入电荷约束，与 SIC 和约束 DFT 计算一起，将允许以一致的方法克服 DFT 和 4G-HDNNP 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，并应用于涵盖合成有机化学重要场景的代表性溶质-溶剂模型系统。