Year-end Sale % OFF 
Abstract
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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
