Machine Learning Boosts the Design and Discovery of Nanomaterials

Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology and NMs largely relies on costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of NMs. ML is a powerful tool for NM research; however, large knowledge gaps and critical issues should be promptly addressed to promote NMs from the laboratory to industry. With a focus on the primary NM aspects, enhancements to the design of NM structures, properties, adsorption, and catalysis by ML are reviewed and discussed. Given the emergent challenges in nanobiology, ML predictions of interactions between NMs and biology are also analyzed. Subsequently, this perspective discusses how to improve the interpretability of ML algorithms, which has been a bottleneck of ML in recent years. ML has led to innovations in the development of NMs, but some problems remain, such as imperfect databases and the accuracy of algorithm determination and nanopattern image recognition, which are herein addressed. Overall, this perspective provides insights for the development of ML in NM research.

Automated summary

The article discusses the application of machine learning in nanomaterial research, emphasizing its importance in big data processing and high-throughput screening, as well as its application in prediction and interaction in nanobiology.