Material discovery and modeling acceleration via machine learning

Abstract

This paper delves into the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in materials science, spotlighting their capability to expedite the discovery and development of newer, more efficient, and stronger compounds. It underscores the shift from traditional, resource-intensive approaches toward data-driven methodologies that leverage large datasets to predict properties, identify new materials, and optimize synthesis conditions with a satisfactory level of accuracy. Highlighting various techniques, including supervised, unsupervised, and reinforcement learning, alongside deep learning potential, the chapter presents case studies and applications ranging from predicting stress points in stochastic fields to optimizing thermal protection systems for spacecraft re-entry. It also explores the challenges and future directions, emphasizing the need for integrating experimental validations and developing tailored algorithms to overcome data and computational constraints. The narrative showcases ML and AI’s promise in revolutionizing material discovery, paving the way for innovative solutions in science and engineering.