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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design:

Anh Tran¹, Julien Tranchida², Tim Wildey¹

¹Optimization and Uncertainty Quantification, Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87123, USA.

The Journal of Chemical Physics

|August 24, 2020

Summary

This study introduces a machine learning framework that combines high-accuracy (density functional theory) and fast (machine learning interatomic potential) atomistic simulations. This approach efficiently predicts materials properties and quantifies prediction uncertainty for faster materials design.

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Area of Science:

Computational Materials Science
Machine Learning in Materials Discovery
Atomistic Simulations

Background:

Accurate prediction of materials properties often requires computationally expensive high-fidelity methods like density functional theory (DFT).
Lower-fidelity methods, such as machine learning interatomic potentials (MLIPs), offer speed but lack accuracy and reliable uncertainty quantification.
Bridging these fidelity gaps is crucial for efficient materials design and discovery.

Purpose of the Study:

To develop and demonstrate a scale-bridging, multi-fidelity machine learning framework for atomistic materials simulations.
To integrate predictions from high-fidelity (DFT) and low-fidelity (MLIP) models.
To enable efficient materials property prediction and uncertainty quantification across alloy composition spaces.

Main Methods:

A multi-fidelity Gaussian process (MFGP) machine learning framework was developed to fuse predictions from DFT and MLIPs.
Uncertainty quantification was achieved through the posterior variance of the MFGP predictions.
The framework was coupled with Bayesian optimization for efficient on-the-fly materials property optimization.

Main Results:

The MFGP framework successfully reproduced the ternary composition dependence of the bulk modulus for aluminum-niobium-titanium random alloys.
The approach demonstrated computational efficiency by performing an on-the-fly search for the global optimum of bulk modulus.
This represents the first application of MFGP to fuse DFT and classical interatomic potential predictions in atomistic materials simulations.

Conclusions:

The presented multi-fidelity machine learning framework effectively bridges scale and fidelity gaps in atomistic simulations.
The method provides accurate property predictions with inherent uncertainty quantification, accelerating materials design.
This approach offers a computationally efficient pathway for exploring complex materials composition spaces.