Numerical Representations of Chemical Data for Structure‐Based Machine Learning
Gyoung S. Na · 2023
The first step to building a scientific application based on machine learning is to numerically represent input data in order to enter the input data into machine learning algorithms. However, the input representation should be carefully designed for successful machine learning because the input representation directly affects the prediction accuracy of the machine learning algorithms. In this chapter, we will explore various input representations from classical feature vectors to composite data formats to understand the benefits of each data representation. In particular, we focus on graph-based input representations that can preserve both the chemical and geometric characteristics of the molecular structures. Also, we will extend the graph-based input representations into general mathematical graphs to represent the molecular structures, the crystal structures, and their composite data formats. In addition to the graph-based representations, we will explore advanced deep neural networks to process such graph data, which is called graph neural networks, and several chemical applications based on graph neural networks will be introduced. As future research, we will discuss how to feed the inter-atomic geometry into graph neural networks to prevent the information loss in geometric information of the molecules. The contents of this chapter will be useful to understand the design principles in constructing chemical databases for successful machine learning.