Machine learning for multiscale modeling in computational molecular design

Abstract

The chemical industry is facing ever-increasing challenges for developing novel products and processes capable of reducing environmental impacts and curbing resource depletion. Yet, the interplay between molecular phenomena and the design of products and processes are often oversimplified. Machine learning stands uniquely positioned to disentangle the complexity of multiscale modeling by leveraging data to navigate the design spaces of multifaceted molecular systems. Herein, we limit our survey of machine learning applications in computational molecular design (CMD) to four elements: property estimation, catalysis, synthesis planning, and design methods. Through this perspective, we aim to offer a roadmap for future work on multiscale modeling that better explores the interplay between nanoscale features and macroscale decisions in product and process design.