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Materials Discovery With Machine Learning and Knowledge Discovery.

Osvaldo N Oliveira1, Maria Cristina F Oliveira2

  • 1Sao Carlos Institute of Physics (IFSC), University of Sao Paulo, Sao Paulo, Brazil.

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|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) and artificial intelligence (AI) are transforming chemistry and materials science. Future applications aim for machine-generated knowledge, but current limitations in interpretation and training need addressing.

Keywords:
computational chemistrydata analyticsknowledge discoverymachine learningmaterials discovery

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Area of Science:

  • Chemistry and Materials Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly utilized in chemistry and materials science.
  • Applications include materials design, discovery, and data analysis from sensors and biosensors.

Purpose of the Study:

  • To provide a perspective on the current use of ML in chemistry and materials science.
  • To discuss the future prospects of ML for knowledge discovery.
  • To identify limitations and propose solutions for broader AI adoption.

Main Methods:

  • Review and perspective on existing literature and applications of ML/AI in the field.
  • Analysis of current limitations in ML for interpretive tasks.
  • Discussion on adapting educational curricula for future scientists.

Main Results:

  • ML/AI show significant promise for accelerating materials design and discovery.
  • Current ML applications are often limited by the need for human interpretation.
  • A gap exists in training and education for effective AI utilization.

Conclusions:

  • Extending ML to knowledge discovery can lead to a new paradigm of machine-generated knowledge.
  • Overcoming interpretation limitations is key to unlocking AI's full potential.
  • Adapting education for chemistry and materials science professionals is crucial for future AI integration.