Machine Learning toward Realizing End-to-End Biochar Design for Environmental Remediation

Abstract

Developing algorithmic methodologies for the rational design of environmental functional materials enables targeted approaches to environmental challenges. Novel machine learning (ML) tools are instrumental in realizing this goal, particularly when biochars are involved with complex components and flexible internal structures. However, such rational design necessitates a holistic perspective across the entire multistage design process, while current ML endeavors for environmental biochar (EB) often concentrate on specific production or application substages. In this regard, taking an end-to-end (E2E) approach to applying ML holds the potential to better guide EB design from a comprehensive view, a perspective yet to be thoroughly explored and summarized. Thus, we review the recent advancements of ML employed in predicting EB problems, aiming to elucidate the broad relevance of various ML models in realizing the E2E design of EBs. It is observed that the properties of EB might be the “Achilles’ heel” within the data set, which poses a significant challenge to achieving the E2E. Furthermore, we also provide an overview of the existing pathways to achieve the E2E, examining both traditional ML and the emerging field of deep leaning, followed by a discussion on key challenges, opportunities, and our vision for the future of rational EB design.