Big data and machine learning for materials science

Discover Materials

Dermot Diamond¹  Jose F. Rodrigues²  Maria C. F. de Oliveira²  Larisa Florea³  Osvaldo N. Oliveira⁴ 

[1] Insight Centre for Data Analytics, National Centre for Sensor Research, Dublin City University;Institute of Mathematical Sciences and Computing, University of São Paulo (USP);SFI Research Centre for Advanced Materials and BioEngineering Research Trinity College Dublin, The University of Dublin;São Carlos Institute of Physics, University of São Paulo (USP);

关键词: Materials discovery;    Big data;    Machine learning;    Deep learning;    Evolutionary algorithms;    Chemical sensors;   

DOI  :  10.1007/s43939-021-00012-0

来源: DOAJ

【 摘 要 】

Abstract Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, 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.

【 授权许可】

Unknown