Machine Learning\ntoward Realizing End-to-End Biochar\nDesign for Environmental Remediation

Developing algorithmic methodologies\nfor the rational design of\nenvironmental functional materials enables targeted approaches to\nenvironmental challenges. Novel machine learning (ML) tools are instrumental\nin realizing this goal, particularly when biochars are involved with\ncomplex components and flexible internal structures. However, such\nrational design necessitates a holistic perspective across the entire\nmultistage design process, while current ML endeavors for environmental\nbiochar (EB) often concentrate on specific production or application\nsubstages. In this regard, taking an end-to-end (E2E) approach to\napplying ML holds the potential to better guide EB design from a comprehensive\nview, a perspective yet to be thoroughly explored and summarized.\nThus, we review the recent advancements of ML employed in predicting\nEB problems, aiming to elucidate the broad relevance of various ML\nmodels in realizing the E2E design of EBs. It is observed that the\nproperties of EB might be the “Achilles’ heel”\nwithin the data set, which poses a significant challenge to achieving\nthe E2E. Furthermore, we also provide an overview of the existing\npathways to achieve the E2E, examining both traditional ML and the\nemerging field of deep leaning, followed by a discussion on key challenges,\nopportunities, and our vision for the future of rational EB design.