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Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

Tarak K Patra1, Venkatesh Meenakshisundaram1, Jui-Hsiang Hung1

  • 1Department of Polymer Engineering, The University of Akron , 250 South Forge Street, Akron, Ohio 44325, United States.

ACS Combinatorial Science
|December 21, 2016
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Summary
This summary is machine-generated.

A new neural-network-biased genetic algorithm (NBGA) accelerates soft materials discovery by learning from limited data. This approach efficiently finds novel materials with extreme properties, overcoming limitations of traditional machine learning methods.

Keywords:
Ising modelcompatibilizergenetic algorithmmachine learningmaterials designmolecular dynamics simulationneural networkoptimizationpolymerssoft matter

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Machine learning (ML) accelerates materials design but struggles with discovering novel soft materials with extremal properties.
  • ML methods are interpolative and require large datasets, which are often unavailable or costly for soft materials.
  • Existing approaches are limited in discovering materials beyond the known range of accessible behavior.

Purpose of the Study:

  • To introduce a novel strategy, the neural-network-biased genetic algorithm (NBGA), for discovering soft materials with extremal properties.
  • To address the limitations of ML in materials design, particularly the need for large datasets and the inability to explore beyond known property ranges.
  • To combine genetic algorithms, ML, and high-throughput methods for efficient and data-efficient materials discovery.

Main Methods:

  • Developed a neural-network-biased genetic algorithm (NBGA) integrating genetic algorithms and artificial neural networks (ANNs).
  • Used progressively constructed ANNs to bias the evolution of the genetic algorithm, guiding the search towards extremal properties.
  • Incorporated direct simulation or experimental evaluations for fitness assessments within the evolutionary process.
  • Tested the NBGA against standard optimization and polymer design problems.

Main Results:

  • The NBGA demonstrated comparable or superior efficiency and reproducibility compared to standard methods.
  • Successfully matched and often exceeded the performance of direct-evaluation genetic algorithms and neural-network-evaluated genetic algorithms.
  • Validated the NBGA's robustness across various optimization and polymer design challenges.

Conclusions:

  • The NBGA provides a robust and efficient strategy for accelerating soft materials design, especially when pre-existing data is scarce.
  • This approach enables the discovery of materials with extremal properties by enabling evolutionary algorithms to learn and infer from experience.
  • The NBGA represents a significant advancement in employing informatics-accelerated high-throughput methods for novel materials discovery.