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Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Bilvin Varughese1,2, Sukriti Manna1,2, Troy D Loeffler1,2

  • 1Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States.

ACS Applied Materials & Interfaces
|April 9, 2024
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Summary
This summary is machine-generated.

Machine learning potentials accurately capture nanoscale and bulk properties by combining transfer and active learning. This approach enhances molecular dynamics simulations for materials design, accelerating discovery across various applications.

Keywords:
NN potentialsactive learningmaterials designnano clusterstransfer learning

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Chemistry and Physics
  • Condensed Matter Physics

Background:

  • Classical molecular dynamics (MD) simulations are vital for materials modeling, but traditional interatomic potentials struggle with nanoscale potential energy surfaces (PESs).
  • Physics-based models often lack flexibility, limiting their accuracy for nanoscale systems compared to first-principles calculations.
  • Machine learning (ML) offers a promising alternative for capturing complex, size-dependent nanoscale phenomena without compromising bulk material properties.

Purpose of the Study:

  • To introduce a novel machine learning workflow for developing high-dimensional neural networks (NNs) for accurate interatomic potentials.
  • To concurrently capture both nanoscale and bulk properties of transition metals using a combination of transfer and active learning strategies.
  • To accelerate materials discovery and design by creating versatile and accurate ML-trained potentials.

Main Methods:

  • Developed a machine learning workflow integrating transfer learning and active learning to create high-dimensional neural networks (NNs).
  • Initial NN training utilized existing high-quality physics-based models for bulk properties, followed by retraining with first-principles data for nanoscale accuracy.
  • Employed a sparsely sampled, diverse dataset covering near-equilibrium to non-equilibrium cluster configurations and iteratively improved model fingerprinting.

Main Results:

  • Successfully developed material-agnostic NNs capable of capturing both cluster and bulk properties for 10 transition metals.
  • Rigorous testing against extensive first-principles data validated the accuracy of energies, forces, and bulk properties.
  • The ML workflow demonstrated effective learning from limited, diverse datasets, improving upon traditional models' limitations.

Conclusions:

  • The proposed ML workflow provides a robust methodology for creating accurate interatomic potentials that bridge the gap between nanoscale and bulk material behavior.
  • This approach enables the transfer of knowledge from established simulations to a new generation of ML-trained potentials, accelerating materials discovery.
  • The material-agnostic nature of the workflow allows for broad applicability in catalysis, microelectronics, and energy storage research.