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Related Experiment Video

Updated: Nov 8, 2025

Advanced Experimental Methods for Low-temperature Magnetotransport Measurement of Novel Materials

Advanced Experimental Methods for Low-temperature Magnetotransport Measurement of Novel Materials

Published on: January 21, 2016

Big data and machine learning for materials science.

Jose F Rodrigues¹, Larisa Florea², Maria C F de Oliveira¹

¹Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil.

Discover Materials

|April 26, 2021

Summary

This summary is machine-generated.

This review explores how big data and machine learning (ML) drive materials science innovation. It highlights ML

Keywords:

Big data Chemical sensors Deep learning Evolutionary algorithms Internet of Things Machine learning Materials discovery

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Area of Science:

Materials Science
Computer Science
Computational Intelligence

Background:

Materials science research increasingly utilizes computational intelligence.
Big data and machine learning (ML) are key drivers of innovation in this field.
ML algorithms require substantial and diverse datasets for effective application.

Purpose of the Study:

To review cutting-edge research in materials science leveraging big data and ML.
To propose a future development roadmap for ML in materials science.
To analyze the conceptual and practical limitations of big data and ML in this domain.

Main Methods:

Literature review of recent advances in materials science and ML.
Analysis of data requirements for ML algorithms in chemical problem-solving.
Examination of case studies on the application of ML in materials science.

Main Results:

Machine learning accelerates solutions to complex chemical problems.
Significant data volumes are necessary for successful ML implementation.
Computer-aided discovery and chemical sensing are prominent ML research areas.

Conclusions:

Big data and ML offer significant potential for materials science discovery.
Understanding limitations and pitfalls is crucial for effective ML application.
Future developments should focus on data generation and algorithm refinement for materials science.