Development of a Neural Network Potential for Metal-Organic Frameworks

金属有机框架神经网络潜力的开发

• 批准号: 405479457
• 负责人: Professor Dr. Jörg Behler
• 金额: --
• 依托单位: Lehrstuhl für Theoretische Chemie II
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/405479457?language=en
• 关键词: Development; Neural; Network; Potential; Metal

A lot of progress has been made in recent years in the development of machine learning (ML) potentials for atomistic simulations. An important class of ML potentials employs artificial neural networks to construct the functional relation between the atomic configuration and the potential energy. To date, neural network potentials (NNP) have been reported for a wide range of materials. They are trained to data from electronic structure calculations and then allow to perform simulations of large systems with the efficiency of simple empirical potentials while maintaining the accuracy of the underlying reference method. In this project the applicability and accuracy of high-dimensional NNPs for organic-inorganic hybrid materials will be investigated, which are very challenging for conventional potentials. For this purpose metal-organic-frameworks (MOFs) will be used as a prototypical and technologically important class of hybrid materials. MOFs consist of metal-oxo clusters that are connected by organic linker molecules to form very stable porous three-dimensional crystalline materials. A particular focus will be on the validation of the NNP that should be applicable to a wide range of MOFs, with implications for the development of potentials for general hybrid systems containing organic and inorganic subsystems.

近年来，在机器学习的发展（ML）的原子模拟潜力方面取得了很多进展。重要的ML电位采用人工神经网络来构建原子构型与势能之间的功能关系。迄今为止，据报道了多种材料的神经网络电位（NNP）。他们接受了电子结构计算的数据训练，然后允许对具有简单经验势能效率的大型系统进行模拟，同时保持基本参考方法的准确性。在该项目中，将研究高维NNP对有机无机杂交材料的适用性和准确性，这对于常规电位非常具有挑战性。为此，金属有机框架（MOF）将用作典型且技术上重要的杂种材料类。 MOF由通过有机接头分子连接的金属氧簇组成，形成非常稳定的多孔三维晶体材料。特别的重点将放在NNP的验证上，该验证应适用于广泛的MOF，这对开发包含有机和无机子系统的一般混合系统的潜力产生了影响。