Four-dimensional photonic micro-actuators for microfluidics applications

Abstract

Herein we review aspects of leading-edge research and innovation in Materials Sciencethatexploits big data and Machine Learning(ML), two computer science conceptsthat combine to yield computationalintelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. But the potential benefits of ML come at the cost of big data production; that is, the algorithmsdemand large volumes of data of various natures and from different sources, from materials properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discoveryof new materials1and analysis of chemical sensingcompounds, both prominent research fields for ML in the context of Materials Science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to Materials Science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.