Publication Date

8-21-2020

Document Type

Article

Department

Mathematics and Statistics

Publication Title

Physical Review Letters

Volume

125

Issue

8

DOI

10.1103/PhysRevLett.125.085503

Abstract

Computational models are formulated in hierarchies of variable fidelity, often with no quantitative rule for defining the fidelity boundaries. We have constructed a dataset from a wide range of atomistic computational models to reveal the accuracy boundary between higher-fidelity models and a simple, lower-fidelity model. The symbolic decision boundary is discovered by optimizing a support vector machine on the data through iterative feature engineering. This data-driven approach reveals two important results: (i) a symbolic rule emerges that is independent of the algorithm, and (ii) the symbolic rule provides a deeper understanding of the fidelity boundary. Specifically, our dataset is composed of radial distribution functions from seven high-fidelity methods that cover wide ranges in the features (element, density, and temperature); high-fidelity results are compared with a simple pair-potential model to discover the nonlinear combination of the features, and the machine learning approach directly reveals the central role of atomic physics in determining accuracy.

Funding Number

FA9550-17-1-0394

Funding Sponsor

Air Force Office of Scientific Research

Comments

This article originally appeared in Physical Review Letters, volume 125, issue 8, 2020, published by the American Physical Society. ©2020 American Physical Society. The article can also be found online at this link.

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