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，在原子模拟的机器学习（ML）潜力的开发方面取得了很大进展。一类重要的ML势采用人工神经网络来构造原子构型与势能之间的函数关系。迄今为止，神经网络电位（NNP）已被广泛报道的材料。它们经过电子结构计算数据的训练，然后允许以简单经验势的效率对大型系统进行模拟，同时保持基础参考方法的准确性。在这个项目中，高维NNPs的适用性和准确性的有机-无机杂化材料将被调查，这是非常具有挑战性的传统潜力。为此目的，金属有机框架（MOFs）将被用作一种典型的和技术上重要的杂化材料。MOFs由金属-氧簇组成，这些金属-氧簇通过有机连接分子连接以形成非常稳定的多孔三维晶体材料。一个特别的重点将是验证的NNP，应适用于范围广泛的MOFs，与发展的潜力一般混合系统包含有机和无机子系统的影响。