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Machine Learning in Nanoscience: Big Data at Small Scales.

Keith A Brown¹, Sarah Brittman², Nicolò Maccaferri³

¹Department of Mechanical Engineering, Physics Department, and Division of Materials Science and Engineering , Boston University , Boston , Massachusetts 02215 , United States.

|December 6, 2019

Summary

This summary is machine-generated.

Machine learning (ML) advances nanoscience by analyzing data and accelerating discovery. Nanoscience, in turn, enables new ML hardware, fostering a synergistic relationship between these fields.

Keywords:

Machine learning active learning data-driven research design of experiments materials discovery

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Area of Science:

Nanoscience
Machine Learning
Materials Science
Neuromorphic Computing

Background:

Machine learning (ML) provides advanced tools for data analysis and efficient data acquisition.
Nanoscience research is increasingly leveraging ML for complex challenges.
Nanoscience is foundational for developing hardware for ML, such as neuromorphic computing.

Purpose of the Study:

To review recent efforts connecting the machine learning and nanoscience communities.
To highlight key areas of interaction and synergy between ML and nanoscience.
To discuss future opportunities and challenges in the ML-nanoscience interface.

Main Methods:

Review of recent literature on ML applications in nanoscience.
Focus on three interaction types: ML for data analysis, ML for material discovery, and nanoscience for ML hardware.
Discussion of active learning in experimental design for materials discovery.

Main Results:

ML effectively analyzes large nanoscience datasets for new insights.
ML accelerates the discovery of novel materials, guided by active learning.
Nanoscience, particularly memristive devices, is crucial for creating tailored ML hardware.

Conclusions:

The synergy between ML and nanoscience offers significant advancements in both fields.
Future research should focus on deepening the integration of ML tools and nanoscience principles.
Addressing challenges will unlock further opportunities for innovation at this interdisciplinary frontier.