Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived quantities

In this chapter, we present a rational design protocol for the discovery and design of new materials and chemistry. With a focus on optical materials with a high refractive index, we describe four components of this protocol that we believe are crucial for its success—molecular library generation, virtual high-throughput screening, database management, and data mining for subsequent analyses. Through a handful of case studies, we demonstrate the effectiveness of our group's software ecosystem, which is aimed at realizing this rational design protocol. In the process, we explore several state-of-the-art machine learning (ML) methods for accelerating the discovery of new materials, such as a multitask, physics-infused deep learning model, and a transfer learning model for tackling the inaccuracies involved with smaller datasets. We show that these models outperform other trivial machine learning models by a significant margin and that incorporating known physics into data-derived models provides valuable guardrails.