Modelling and understanding the structure of graphene oxide materials with machine-learning-driven simulations

石墨烯是一种具有蜂窝结构的二维碳原子片，已经从学术兴趣发展到大规模工业生产。石墨烯本身在元素上是纯的，但存在许多化学修饰的材料，统称为“氧化石墨烯”（GO）。在氧化石墨烯中，石墨烯蜂窝被各种类型的氢基和氧基官能团（“羟基”、“环氧”和其他类似于有机分子中的官能团）分解。氧化石墨烯中的官能团不仅具有根本意义，而且被认为主要负责实际应用-例如，在催化中，它们定义了一个分子转化为另一个分子的活性中心。石墨烯本身具有有序的原子结构，但氧化石墨烯则不然，因为其结构和化学多样性很大。直到今天，这种结构还没有被完全理解，这严重阻碍了基础研究和GO的商业开发的进展。本项目的目的是开发和部署一种新的计算机模拟方法，基于精确量子力学数据的机器学习（ML），以一种以前不可能的方式了解氧化石墨烯的原子结构。一个模拟结构的数字“图书馆”将被系统地组装起来，以比现有模拟方法更现实的细节代表可能的化学修饰的范围。该项目将导致一个新的原子间势（“力场”），研究人员可以用它来模拟原子尺度上的氧化石墨烯。该项目还将预测氧化石墨烯材料微观结构的精确计算光谱指纹目录：这将有助于破译学术和工业实验室中每天使用的各种实验测量结果。结果数据集将公开分发，以最大限度地提高该项目的学术影响。目前的研究本质上是理论和计算的，但它有望具有直接的实际意义：通过允许化学家，物理学家和材料科学家一方面在氧化石墨烯的原子结构之间建立新的联系，另一方面在技术上相关的性质。因此，它是计算化学如何在新的机器学习方法的加速下，在复杂功能材料的建模中达到以前无法达到的现实程度的一个例子。

Graphene, a two-dimensional sheet of carbon atoms with a honeycomb structure, has evolved from academic interest to large-scale industrial production. Graphene itself is elementally pure, but there exists a wide range of chemically modified materials that are summarily called "graphene oxide" (GO). In GO, the graphene honeycomb is broken up by various types of hydrogen- and oxygen-based functional groups ("hydroxyl", "epoxy", and others, similar to what is found in organic molecules). The functional groups in GO are not just of fundamental interest, but they are thought to be primarily responsible for practical applications - for example, in catalysis, where they define the active centres at which one molecule is transformed into another.Graphene itself has an ordered atomic structure, but GO much less so, because of the large structural and chemical diversity. This structure is incompletely understood until today, and this has strongly hindered the progress of fundamental research and the commercial exploitation of GO alike. The aim of the present project is to develop and deploy a new computer simulation methodology, based on machine learning (ML) from accurate quantum-mechanical data, to understand the atomic structure of GO in a way that was not previously possible. A digital "library" of simulated structures will be systematically assembled, representing the range of possible chemical modifications in much more realistic detail than established simulation methods could afford. The project will lead to a new interatomic potential ("force field") that researchers can use to simulate GO on the atomistic scale. The project will also predict a catalogue of accurately computed spectroscopic fingerprints for the microscopic structure of GO materials: this will help to decipher the outcome of various experimental measurements that are used every day in academic and industrial laboratories. The resulting datasets will be openly distributed to maximise the project's academic impact.The present research is theoretical and computational in nature, but it is expected to have a direct practical implication: by allowing chemists, physicists, and materials scientists to establish new connections between the atomistic structure of GO on the one hand, and technologically relevant properties on the other hand. It is therefore an example for how computational chemistry, accelerated by new machine-learning approaches, can reach a previously unavailable degree of realism in the modelling of complex functional materials.