Study on Emerging Machine Learning Trends on Nanoparticles—Nanoinformatics

Abstract

Artificial intelligence (AI) is an influential technology, which has helped many branches of science and technology accomplish an unimaginable progress. The field of nanoparticles has had more than a decade-long tryst with AI, but it is yet to fully realize the benefits of AI to the extent other branches of technologies have. In recent times, there has been a renewed interest in applying machine learning and similar AI concepts to produce new nanoparticles, which has resulted in exponential growth of research in that direction and resulting data. This research paper critically analyzes that data to explore the prevalent trends/methods in applying machine learning (ML) and data mining techniques in the field of nanoparticles. Beginning with delineating the challenges faced in implementing ML in the nanoparticles segment, this paper goes on to explore the various learning algorithms relevant to the field of nanomaterials, their applications, suitability, merits, and demerits. In the latter section of the paper, a detailed analysis (in the form of a flow chart) spells out the considerations to be taken while collecting and constructing data, choosing and applying appropriate ML algorithms. This paper reviews various ML algorithms used in recent articles. In essence, this paper compendiously provides information and prediction of nanoparticles necessary to successfully customize ML for nanomaterials vertical, especially for nanoparticles of medical significance and applications.KeywordsMachine learningArtificial intelligenceNanoparticlesData mining