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.

机器学习潜力（MLP）已成为对以电子结构方法准确的计算成本进行精确进行冷凝系统进行原子模拟的重要工具。迄今为止，大多数应用都在材料科学中报道，而有机分子主要是为了实现真空的基准目的而研究的。尽管大多数化学反应发生在液相中，但MLP在溶液中的应用和分子化学在溶液中的应用仍然非常罕见。除了相关配置空间的复杂性外，研究这些系统的主要挑战是需要对内部内部和分子间相互作用进行高度准确的描述，从通过氢键通过氢键到静电和分散相互作用的强共价键。一个特别关键的方面是相关物种中的电荷分布，大多数当前MLP无法根据局部特性正确捕获，基于环境依赖性的原子能和电荷，我们已经开发出了第四代高维神经网络电位（4G-HDNNP）（4G-HDNNP），从而将基于局部互动的准确描述与整体互动相结合。这种全局描述不仅对于包含DELEACALIZED电子的分子，例如在芳香族组或共轭的PI系统中，但如果分子电荷正在变化，例如（DE）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由4G-HDNNP研究，该系统明确考虑了由反应，不同官能团和不同总电荷产生的全球电荷分布，这使该方法成为分子化学的有前途的方法。该项目的目的是通过关注两个主要方面，即密度功能理论（DFT）参考参考文献的质量以及4G-HDNNP方法的概括，探索4G-HDNNP对溶液中分子化学的适用性。将通过基准对量子蒙特卡洛和耦合群集计算的广义梯度近似（GGA）水平以及包括分散和自我相互作用校正（SIC）进行基准，将交换相关函数的可靠性基准基准来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歧视，甚至适用于诸如圆锥形交叉点之类的困难情况，并引入了电荷约束，这些歧视将与SIC和约束DFT计算一起，允许在一致的方法中克服dft，DFT和4G-HDNNN的整数收费问题。这组新的计算工具将在开源软件跑步者中实现，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重要方案。