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.

机器学习电位（MLP）已经成为一个重要的工具，用于执行凝聚态系统的原子模拟与精度的电子结构方法在一小部分的计算成本。迄今为止，大多数应用已经在材料科学中报道，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MLPs在溶液溶剂化和分子化学中的应用仍然很少。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以及分子间的相互作用，从通过氢键形成的强共价键到静电和分散相互作用。一个特别重要的方面是所涉及的物种中的电荷分布，这是目前大多数基于环境依赖的原子能和电荷等局部特性的mlp无法正确捕获的。最近，我们开发了第四代高维神经网络电位（4G-HDNNP），它结合了局部键和反应性的准确描述以及基于系统中全局电荷分布的远程相互作用。这种全局描述不仅对含有离域电子的分子（如芳香基团或共轭pi体系）是必要的，而且对分子电荷发生变化的分子（如去质子化）也是必要的，这是有机化学中许多类型反应的关键步骤。所有这些体系原则上都可以通过4G-HDNNPs进行研究，该方法明确考虑了由反应、不同官能团和总电荷变化引起的全局电荷分布，使该方法成为分子化学的一种很有前途的方法。本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，探索4G-HDNNP在溶液分子化学中的适用性。通过对量子蒙特卡罗和耦合聚类计算中超越广义梯度近似（GGA）级别的交换相关函数的可靠性进行基准测试，并通过包括色散和自相互作用修正（SIC），将获得高质量的参考数据。4G-HDNNP将通过采用新的描述符类型来扩展结构判别，甚至适用于锥形相交等困难情况，并通过引入电荷约束，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。